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Leveraging Visibility Graphs for Enhanced Arrhythmia Classification with Graph Convolutional Networks (2404.15367v2)
Published 19 Apr 2024 in eess.SP, cs.CV, and cs.LG
Abstract: Arrhythmias, detectable through electrocardiograms (ECGs), pose significant health risks, underscoring the need for accurate and efficient automated detection techniques. While recent advancements in graph-based methods have demonstrated potential to enhance arrhythmia classification, the challenge lies in effectively representing ECG signals as graphs. This study investigates the use of Visibility Graph (VG) and Vector Visibility Graph (VVG) representations combined with Graph Convolutional Networks (GCNs) for arrhythmia classification under the ANSI/AAMI standard, ensuring reproducibility and fair comparison with other techniques. Through extensive experiments on the MIT-BIH dataset, we evaluate various GCN architectures and preprocessing parameters. Our findings demonstrate that VG and VVG mappings enable GCNs to classify arrhythmias directly from raw ECG signals, without the need for preprocessing or noise removal. Notably, VG offers superior computational efficiency, while VVG delivers enhanced classification performance by leveraging additional lead features. The proposed approach outperforms baseline methods in several metrics, although challenges persist in classifying the supraventricular ectopic beat (S) class, particularly under the inter-patient paradigm.
- Pandey, S.K., Janghel, R.R.: Automatic detection of arrhythmia from imbalanced ecg database using cnn model with smote. Australasian physical & engineering sciences in medicine 42(4), 1129–1139 (2019) Cheng and Dong [2017] Cheng, P., Dong, X.: Life-threatening ventricular arrhythmia detection with personalized features. IEEE access 5, 14195–14203 (2017) Luz et al. [2016] Luz, E.J.d.S., Schwartz, W.R., Cámara-Chávez, G., Menotti, D.: Ecg-based heartbeat classification for arrhythmia detection: A survey. Computer methods and programs in biomedicine 127, 144–164 (2016) Zaorálek et al. [2018] Zaorálek, L., Platoš, J., Snášel, V.: Patient-adapted and inter-patient ecg classification using neural network and gradient boosting. Neural Network World 28(3), 241–254 (2018) Hannun et al. [2019] Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 25(1), 65 (2019) ANSI/AAMI [2008] ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cheng, P., Dong, X.: Life-threatening ventricular arrhythmia detection with personalized features. IEEE access 5, 14195–14203 (2017) Luz et al. [2016] Luz, E.J.d.S., Schwartz, W.R., Cámara-Chávez, G., Menotti, D.: Ecg-based heartbeat classification for arrhythmia detection: A survey. Computer methods and programs in biomedicine 127, 144–164 (2016) Zaorálek et al. [2018] Zaorálek, L., Platoš, J., Snášel, V.: Patient-adapted and inter-patient ecg classification using neural network and gradient boosting. Neural Network World 28(3), 241–254 (2018) Hannun et al. [2019] Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 25(1), 65 (2019) ANSI/AAMI [2008] ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luz, E.J.d.S., Schwartz, W.R., Cámara-Chávez, G., Menotti, D.: Ecg-based heartbeat classification for arrhythmia detection: A survey. Computer methods and programs in biomedicine 127, 144–164 (2016) Zaorálek et al. [2018] Zaorálek, L., Platoš, J., Snášel, V.: Patient-adapted and inter-patient ecg classification using neural network and gradient boosting. Neural Network World 28(3), 241–254 (2018) Hannun et al. [2019] Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 25(1), 65 (2019) ANSI/AAMI [2008] ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zaorálek, L., Platoš, J., Snášel, V.: Patient-adapted and inter-patient ecg classification using neural network and gradient boosting. Neural Network World 28(3), 241–254 (2018) Hannun et al. [2019] Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 25(1), 65 (2019) ANSI/AAMI [2008] ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 25(1), 65 (2019) ANSI/AAMI [2008] ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. 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IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Cheng, P., Dong, X.: Life-threatening ventricular arrhythmia detection with personalized features. IEEE access 5, 14195–14203 (2017) Luz et al. [2016] Luz, E.J.d.S., Schwartz, W.R., Cámara-Chávez, G., Menotti, D.: Ecg-based heartbeat classification for arrhythmia detection: A survey. Computer methods and programs in biomedicine 127, 144–164 (2016) Zaorálek et al. [2018] Zaorálek, L., Platoš, J., Snášel, V.: Patient-adapted and inter-patient ecg classification using neural network and gradient boosting. Neural Network World 28(3), 241–254 (2018) Hannun et al. [2019] Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 25(1), 65 (2019) ANSI/AAMI [2008] ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luz, E.J.d.S., Schwartz, W.R., Cámara-Chávez, G., Menotti, D.: Ecg-based heartbeat classification for arrhythmia detection: A survey. Computer methods and programs in biomedicine 127, 144–164 (2016) Zaorálek et al. [2018] Zaorálek, L., Platoš, J., Snášel, V.: Patient-adapted and inter-patient ecg classification using neural network and gradient boosting. Neural Network World 28(3), 241–254 (2018) Hannun et al. [2019] Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 25(1), 65 (2019) ANSI/AAMI [2008] ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zaorálek, L., Platoš, J., Snášel, V.: Patient-adapted and inter-patient ecg classification using neural network and gradient boosting. Neural Network World 28(3), 241–254 (2018) Hannun et al. [2019] Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 25(1), 65 (2019) ANSI/AAMI [2008] ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 25(1), 65 (2019) ANSI/AAMI [2008] ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. 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[2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. 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[2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zaorálek, L., Platoš, J., Snášel, V.: Patient-adapted and inter-patient ecg classification using neural network and gradient boosting. Neural Network World 28(3), 241–254 (2018) Hannun et al. [2019] Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 25(1), 65 (2019) ANSI/AAMI [2008] ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 25(1), 65 (2019) ANSI/AAMI [2008] ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. 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IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Zaorálek, L., Platoš, J., Snášel, V.: Patient-adapted and inter-patient ecg classification using neural network and gradient boosting. Neural Network World 28(3), 241–254 (2018) Hannun et al. [2019] Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 25(1), 65 (2019) ANSI/AAMI [2008] ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 25(1), 65 (2019) ANSI/AAMI [2008] ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. 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IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Hannun, A.Y., Rajpurkar, P., Haghpanahi, M., Tison, G.H., Bourn, C., Turakhia, M.P., Ng, A.Y.: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature medicine 25(1), 65 (2019) ANSI/AAMI [2008] ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. 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IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- ANSI/AAMI: Testing and Reporting Performance Results of Cardiac Rhythm and ST Segment Measurement Algorithms. American National Standards Institute, Inc. (ANSI), Association for the Advancement of Medical Instrumentation (AAMI). ANSI/AAMI/ISO EC57, 1998-(R)2008 (2008) De Chazal et al. [2004] De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- De Chazal, P., O’Dwyer, M., Reilly, R.B.: Automatic classification of heartbeats using ecg morphology and heartbeat interval features. IEEE transactions on biomedical engineering 51(7), 1196–1206 (2004) Luz and Menotti [2011] Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. 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[2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luz, E., Menotti, D.: How the choice of samples for building arrhythmia classifiers impact their performances. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4988–4991 (2011). IEEE Mousavi and Afghah [2019] Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
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Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Mousavi, S., Afghah, F.: Inter-and intra-patient ecg heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach. In: ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1308–1312 (2019). IEEE Essa and Xie [2021] Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. 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IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Essa, E., Xie, X.: An ensemble of deep learning-based multi-model for ecg heartbeats arrhythmia classification. IEEE Access 9, 103452–103464 (2021) Garcia et al. [2017] Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Garcia, G., Moreira, G., Menotti, D., Luz, E.: Inter-patient ecg heartbeat classification with temporal vcg optimized by pso. Scientific reports 7(1), 1–11 (2017) Barabási [2013] Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. 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IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. 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[2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
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[2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. 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[2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Barabási, A.-L.: Network science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 371(1987), 20120375 (2013) Ren and Jin [2019] Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
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[2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. 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[1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. 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IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Ren, W., Jin, N.: Vector visibility graph from multivariate time series: a new method for characterizing nonlinear dynamic behavior in two-phase flow. Nonlinear Dynamics 97, 2547–2556 (2019) Zhang and Small [2006] Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhang, J., Small, M.: Complex network from pseudoperiodic time series: Topology versus dynamics. Physical review letters 96(23), 238701 (2006) Sun et al. [2014] Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
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IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sun, X., Small, M., Zhao, Y., Xue, X.: Characterizing system dynamics with a weighted and directed network constructed from time series data. Chaos: An Interdisciplinary Journal of Nonlinear Science 24(2), 024402 (2014) Donner et al. [2010] Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). 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IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. 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IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Donner, R.V., Zou, Y., Donges, J.F., Marwan, N., Kurths, J.: Recurrence networks—a novel paradigm for nonlinear time series analysis. New Journal of Physics 12(3), 033025 (2010) Donges et al. [2011] Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Donges, J.F., Donner, R.V., Trauth, M.H., Marwan, N., Schellnhuber, H.-J., Kurths, J.: Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution. Proceedings of the National Academy of Sciences 108(51), 20422–20427 (2011) Lacasa et al. [2008] Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. 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[2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuno, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences 105(13), 4972–4975 (2008) Luque et al. [2009] Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009) Gotoda et al. [2017] Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. 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In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. 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[2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. 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IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Gotoda, H., Kinugawa, H., Tsujimoto, R., Domen, S., Okuno, Y.: Characterization of combustion dynamics, detection, and prevention of an unstable combustion state based on a complex-network theory. Physical Review Applied 7(4), 044027 (2017) Freitas et al. [2019] Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Freitas, V.L.S., Lacerda, J.C., Macau, E.E.N.: Complex Networks Approach for Dynamical Characterization of Nonlinear Systems. International Journal of Bifurcation and Chaos 29(13), 1950188–512 (2019) https://doi.org/10.1142/S0218127419501888 Zhou et al. [2020] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. 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IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
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[1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. 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[2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: A review of methods and applications. AI Open 1, 57–81 (2020) Sperduti and Starita [1997] Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. 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[2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. 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[2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. 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[2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. 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[2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. 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IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. 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[2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Transactions on Neural Networks 8(3), 714–735 (1997) Scarselli et al. [2008] Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. 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IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE transactions on neural networks 20(1), 61–80 (2008) Micheli [2009] Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. 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[2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Micheli, A.: Neural network for graphs: A contextual constructive approach. IEEE Transactions on Neural Networks 20(3), 498–511 (2009) LeCun et al. [1998] LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998) Wu et al. [2020] Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems 32(1), 4–24 (2020) Shuman et al. [2013] Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
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[2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30(3), 83–98 (2013) Kipf and Welling [2016] Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016) Hamilton et al. [2017] Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
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[2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017) Cao et al. [2023] Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
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In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Cao, M., Zhao, T., Li, Y., Zhang, W., Benharash, P., Ramezani, R.: Ecg heartbeat classification using deep transfer learning with convolutional neural network and stft technique. In: Journal of Physics: Conference Series, vol. 2547, p. 012031 (2023). IOP Publishing Gai [2022] Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Gai, N.D.: Ecg beat classification using machine learning and pre-trained convolutional neural networks. arXiv preprint arXiv:2207.06408 (2022) Mathews et al. [2018] Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Mathews, S.M., Kambhamettu, C., Barner, K.E.: A novel application of deep learning for single-lead ecg classification. Computers in biology and medicine 99, 53–62 (2018) Zhao et al. [2022] Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Zhao, X., Liu, Z., Han, L., Peng, S.: Ecgnn: Enhancing abnormal recognition in 12-lead ecg with graph neural network. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1411–1416 (2022). IEEE Duong et al. [2023] Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Duong, L.T., Doan, T.T.H., Chu, C.Q., Nguyen, P.T.: Fusion of edge detection and graph neural networks to classifying electrocardiogram signals. Expert Systems with Applications 225, 120107 (2023) https://doi.org/10.1016/j.eswa.2023.120107 Moody and Mark [1990] Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. 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[2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. 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[2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
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[2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The mit-bih arrhythmia database on cd-rom and software for use with it. In: [1990] Proceedings Computers in Cardiology, pp. 185–188 (1990). IEEE Moody and Mark [2001] Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Moody, G.B., Mark, R.G.: The impact of the mit-bih arrhythmia database. IEEE Engineering in Medicine and Biology Magazine 20(3), 45–50 (2001) Sraitih et al. [2021] Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Sraitih, M., Jabrane, Y., Atlas, A.: An overview on intra-and inter-patient paradigm for ecg heartbeat arrhythmia classification. In: 2021 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), pp. 1–7 (2021). IEEE Kojima et al. [2020] Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
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- Kojima, R., Ishida, S., Ohta, M., Iwata, H., Honma, T., Okuno, Y.: kgcn: a graph-based deep learning framework for chemical structures. Journal of Cheminformatics 12(1), 1–10 (2020) Xu et al. [2018] Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018) Shobanadevi and Veeramakali [2023] Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023) Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)
- Shobanadevi, A., Veeramakali, T.: Classification and interpretation of ecg arrhythmia through deep learning techniques (2023)