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The OxMat dataset: a multimodal resource for the development of AI-driven technologies in maternal and newborn child health (2404.08024v1)
Published 11 Apr 2024 in cs.LG
Abstract: The rapid advancement of AI in healthcare presents a unique opportunity for advancements in obstetric care, particularly through the analysis of cardiotocography (CTG) for fetal monitoring. However, the effectiveness of such technologies depends upon the availability of large, high-quality datasets that are suitable for machine learning. This paper introduces the Oxford Maternity (OxMat) dataset, the world's largest curated dataset of CTGs, featuring raw time series CTG data and extensive clinical data for both mothers and babies, which is ideally placed for machine learning. The OxMat dataset addresses the critical gap in women's health data by providing over 177,211 unique CTG recordings from 51,036 pregnancies, carefully curated and reviewed since 1991. The dataset also comprises over 200 antepartum, intrapartum and postpartum clinical variables, ensuring near-complete data for crucial outcomes such as stillbirth and acidaemia. While this dataset also covers the intrapartum stage, around 94% of the constituent CTGS are antepartum. This allows for a unique focus on the underserved antepartum period, in which early detection of at-risk fetuses can significantly improve health outcomes. Our comprehensive review of existing datasets reveals the limitations of current datasets: primarily, their lack of sufficient volume, detailed clinical data and antepartum data. The OxMat dataset lays a foundation for future AI-driven prenatal care, offering a robust resource for developing and testing algorithms aimed at improving maternal and fetal health outcomes.
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European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. 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[2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. 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[2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Qureshi, R., Irfan, M., Ali, H., Khan, A., Nittala, A.S., Ali, S., Shah, A., Gondal, T.M., Sadak, F., Shah, Z., Hadi, M.U., Khan, S., Al-Tashi, Q., Wu, J., Bermak, A., Alam, T.: Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review. IEEE Access 11, 61600–61620 (2023) https://doi.org/10.1109/ACCESS.2023.3285596 Manickam et al. [2022] Manickam, P., Mariappan, S.A., Murugesan, S.M., Hansda, S., Kaushik, A., Shinde, R., Thipperudraswamy, S.P.: Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. Biosensors 12(8) (2022) https://doi.org/10.3390/BIOS12080562 Junaid et al. [2022] Junaid, S.B., Imam, A.A., Abdulkarim, M., Surakat, Y.A., Balogun, A.O., Kumar, G., Shuaibu, A.N., Garba, A., Sahalu, Y., Mohammed, A., Mohammed, T.Y., Abdulkadir, B.A., Abba, A.A., Iliyasu Kakumi, N.A., Hashim, A.S.: Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery. Applied Sciences 2022, Vol. 12, Page 10271 12(20), 10271 (2022) https://doi.org/10.3390/APP122010271 Choy et al. [2018] Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A.E., Pianykh, O.S., Geis, J.R., Pandharipande, P.V., Brink, J.A., Dreyer, K.J.: Current applications and future impact of machine learning in radiology. Radiology 288(2), 318–328 (2018) https://doi.org/10.1148/RADIOL.2018171820/ASSET/IMAGES/LARGE/RADIOL.2018171820.FIG8.JPEG Rodriguez-Ruiz et al. [2019] Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T.H., Chevalier, M., Tan, T., Mertelmeier, T., Wallis, M.G., Andersson, I., Zackrisson, S., Mann, R.M., Sechopoulos, I.: Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. JNCI: Journal of the National Cancer Institute 111(9), 916–922 (2019) https://doi.org/10.1093/JNCI/DJY222 Stirnemann et al. [2023] Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. 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[2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. 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Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. 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[2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. 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IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. 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[2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. 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The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. 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[2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. 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[2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Manickam, P., Mariappan, S.A., Murugesan, S.M., Hansda, S., Kaushik, A., Shinde, R., Thipperudraswamy, S.P.: Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. Biosensors 12(8) (2022) https://doi.org/10.3390/BIOS12080562 Junaid et al. 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ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. 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[2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A.E., Pianykh, O.S., Geis, J.R., Pandharipande, P.V., Brink, J.A., Dreyer, K.J.: Current applications and future impact of machine learning in radiology. Radiology 288(2), 318–328 (2018) https://doi.org/10.1148/RADIOL.2018171820/ASSET/IMAGES/LARGE/RADIOL.2018171820.FIG8.JPEG Rodriguez-Ruiz et al. 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ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. 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Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. 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The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T.H., Chevalier, M., Tan, T., Mertelmeier, T., Wallis, M.G., Andersson, I., Zackrisson, S., Mann, R.M., Sechopoulos, I.: Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. JNCI: Journal of the National Cancer Institute 111(9), 916–922 (2019) https://doi.org/10.1093/JNCI/DJY222 Stirnemann et al. [2023] Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. 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[2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. 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[2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. 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[2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. 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[2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. 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[2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Manickam, P., Mariappan, S.A., Murugesan, S.M., Hansda, S., Kaushik, A., Shinde, R., Thipperudraswamy, S.P.: Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. Biosensors 12(8) (2022) https://doi.org/10.3390/BIOS12080562 Junaid et al. 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ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. 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[2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A.E., Pianykh, O.S., Geis, J.R., Pandharipande, P.V., Brink, J.A., Dreyer, K.J.: Current applications and future impact of machine learning in radiology. Radiology 288(2), 318–328 (2018) https://doi.org/10.1148/RADIOL.2018171820/ASSET/IMAGES/LARGE/RADIOL.2018171820.FIG8.JPEG Rodriguez-Ruiz et al. 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ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. 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Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. 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The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T.H., Chevalier, M., Tan, T., Mertelmeier, T., Wallis, M.G., Andersson, I., Zackrisson, S., Mann, R.M., Sechopoulos, I.: Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. JNCI: Journal of the National Cancer Institute 111(9), 916–922 (2019) https://doi.org/10.1093/JNCI/DJY222 Stirnemann et al. [2023] Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. 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[2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. 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[2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. 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[2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. 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[2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. 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[2019] Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T.H., Chevalier, M., Tan, T., Mertelmeier, T., Wallis, M.G., Andersson, I., Zackrisson, S., Mann, R.M., Sechopoulos, I.: Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. JNCI: Journal of the National Cancer Institute 111(9), 916–922 (2019) https://doi.org/10.1093/JNCI/DJY222 Stirnemann et al. [2023] Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. 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The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. 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[2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Junaid, S.B., Imam, A.A., Abdulkarim, M., Surakat, Y.A., Balogun, A.O., Kumar, G., Shuaibu, A.N., Garba, A., Sahalu, Y., Mohammed, A., Mohammed, T.Y., Abdulkadir, B.A., Abba, A.A., Iliyasu Kakumi, N.A., Hashim, A.S.: Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery. 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[2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. 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IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. 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IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. 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The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Junaid, S.B., Imam, A.A., Abdulkarim, M., Surakat, Y.A., Balogun, A.O., Kumar, G., Shuaibu, A.N., Garba, A., Sahalu, Y., Mohammed, A., Mohammed, T.Y., Abdulkadir, B.A., Abba, A.A., Iliyasu Kakumi, N.A., Hashim, A.S.: Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery. Applied Sciences 2022, Vol. 12, Page 10271 12(20), 10271 (2022) https://doi.org/10.3390/APP122010271 Choy et al. [2018] Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A.E., Pianykh, O.S., Geis, J.R., Pandharipande, P.V., Brink, J.A., Dreyer, K.J.: Current applications and future impact of machine learning in radiology. Radiology 288(2), 318–328 (2018) https://doi.org/10.1148/RADIOL.2018171820/ASSET/IMAGES/LARGE/RADIOL.2018171820.FIG8.JPEG Rodriguez-Ruiz et al. [2019] Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T.H., Chevalier, M., Tan, T., Mertelmeier, T., Wallis, M.G., Andersson, I., Zackrisson, S., Mann, R.M., Sechopoulos, I.: Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. JNCI: Journal of the National Cancer Institute 111(9), 916–922 (2019) https://doi.org/10.1093/JNCI/DJY222 Stirnemann et al. 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European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. 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The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. 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IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. 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IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. 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[2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Manickam, P., Mariappan, S.A., Murugesan, S.M., Hansda, S., Kaushik, A., Shinde, R., Thipperudraswamy, S.P.: Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare. Biosensors 12(8) (2022) https://doi.org/10.3390/BIOS12080562 Junaid et al. 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ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. 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[2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A.E., Pianykh, O.S., Geis, J.R., Pandharipande, P.V., Brink, J.A., Dreyer, K.J.: Current applications and future impact of machine learning in radiology. Radiology 288(2), 318–328 (2018) https://doi.org/10.1148/RADIOL.2018171820/ASSET/IMAGES/LARGE/RADIOL.2018171820.FIG8.JPEG Rodriguez-Ruiz et al. 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ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. 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Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. 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The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T.H., Chevalier, M., Tan, T., Mertelmeier, T., Wallis, M.G., Andersson, I., Zackrisson, S., Mann, R.M., Sechopoulos, I.: Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. JNCI: Journal of the National Cancer Institute 111(9), 916–922 (2019) https://doi.org/10.1093/JNCI/DJY222 Stirnemann et al. [2023] Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. 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[2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. 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BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141
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Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. 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[2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. 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[2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. 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[2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T.H., Chevalier, M., Tan, T., Mertelmeier, T., Wallis, M.G., Andersson, I., Zackrisson, S., Mann, R.M., Sechopoulos, I.: Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. JNCI: Journal of the National Cancer Institute 111(9), 916–922 (2019) https://doi.org/10.1093/JNCI/DJY222 Stirnemann et al. [2023] Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. 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[2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. 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[2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. 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[2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. 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ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. 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JNCI: Journal of the National Cancer Institute 111(9), 916–922 (2019) https://doi.org/10.1093/JNCI/DJY222 Stirnemann et al. [2023] Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. 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[2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. 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[2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Rodriguez-Ruiz, A., Lång, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., Helbich, T.H., Chevalier, M., Tan, T., Mertelmeier, T., Wallis, M.G., Andersson, I., Zackrisson, S., Mann, R.M., Sechopoulos, I.: Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. JNCI: Journal of the National Cancer Institute 111(9), 916–922 (2019) https://doi.org/10.1093/JNCI/DJY222 Stirnemann et al. [2023] Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. 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[2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. 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[2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. 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[2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. 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ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. 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The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Stirnemann, J.J., Besson, R., Spaggiari, E., Rojo, S., Loge, F., Peyro-Saint-Paul, H., Allassonniere, S., Le Pennec, E., Hutchinson, C., Sebire, N., Ville, Y.: Development and clinical validation of real-time artificial intelligence diagnostic companion for fetal ultrasound examination. Ultrasound in Obstetrics & Gynecology 62(3), 353–360 (2023) https://doi.org/10.1002/UOG.26242 Morid et al. [2023] Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. 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[2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. 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IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. 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The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. 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[2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. 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IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. 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[2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. 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[2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. 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[2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141
- Morid, M.A., Sheng, O.R.L., Dunbar, J.: Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 14(1) (2023) https://doi.org/10.1145/3531326 [14] Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. 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[2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Closing the Women’s Health Gap to Improve Lives and Economies — World Economic Forum. https://www.weforum.org/publications/closing-the-women-s-health-gap-a-1-trillion-opportunity-to-improve-lives-and-economies/ Pardey et al. [2002] Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. 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[2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. 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Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141
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Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Pardey, J., Moulden, M., Redman, C.W.G.: A computer system for the numerical analysis of nonstress tests. American Journal of Obstetrics and Gynecology 186(5), 1095–1103 (2002) https://doi.org/10.1067/MOB.2002.122447 Ayres-De-Campos et al. [2015] Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Ayres-De-Campos, D., Spong, C.Y., Chandraharan, E.: FIGO consensus guidelines on intrapartum fetal monitoring: Cardiotocography. International Journal of Gynecology & Obstetrics 131(1), 13–24 (2015) https://doi.org/10.1016/J.IJGO.2015.06.020 Alfirevic et al. [2017] Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. 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[2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. 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IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. 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IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. 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[2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Alfirevic, Z., Devane, D., Gyte, G.M.L., Cuthbert, A.: Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour. Cochrane Database of Systematic Reviews 2017(2) (2017) https://doi.org/10.1002/14651858.CD006066.PUB3/MEDIA/CDSR/CD006066/IMAGE{_}N/NCD006066-CMP-002-02. Al-yousif et al. [2021] Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Al-yousif, S., Jaenul, A., Al-Dayyeni, W., Alamoodi, A., Jabori, I., Tahir, N.M., Alrawi, A.A.A., Cömert, Z., Al-shareefi, N.A., Saleh, A.H.: A systematic review of automated pre-processing, feature extraction and classification of cardiotocography. PeerJ Computer Science 7, 1–37 (2021) https://doi.org/10.7717/PEERJ-CS.452 Aeberhard et al. [2024] Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. 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The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. 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[2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.-P., Soltani, R.A., Strahm, K.M., Schneider, S., Carrié, A., Lemay, M., Krauss, J., Delgado-Gonzalo, R., Surbek, D.: Introducing Artificial Intelligence in Interpretation of Foetal Cardiotocography: Medical Dataset Curation and Preliminary Coding—An Interdisciplinary Project. Methods and Protocols 2024, Vol. 7, Page 5 7(1), 5 (2024) https://doi.org/10.3390/MPS7010005 Aeberhard et al. [2023] Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. 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The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Aeberhard, J.L., Radan, A.P., Delgado-Gonzalo, R., Strahm, K.M., Sigurthorsdottir, H.B., Schneider, S., Surbek, D.: Artificial intelligence and machine learning in cardiotocography: A scoping review. European Journal of Obstetrics & Gynecology and Reproductive Biology 281, 54–62 (2023) https://doi.org/10.1016/J.EJOGRB.2022.12.008 Barnova et al. [2024] Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. [2017] Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. 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[2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. 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[2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141
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IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Barnova, K., Martinek, R., Vilimkova Kahankova, R., Jaros, R., Snasel, V., Mirjalili, S.: Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring. Archives of Computational Methods in Engineering 2024, 1–32 (2024) https://doi.org/10.1007/S11831-023-10055-6 Brocklehurst et al. 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The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. 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Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D., Greene, K., Juszczak, E., Kenyon, S., Linsell, L., Mabey, C., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, E., Steer, P., Keith, R., Johns, N., Johnston, T., Barnfield, G., Davies, K., Johnson, M., Patterson, H., Montague, I., Watmore, S., Stolton, A., Parisaei, M., McGhee, N., Segovia, S., Martindale, E., Jackson, H., Holleran, J., Roberts, D., Holt, S., Dragovic, B., Willmott-Powell, M., Hutchinson, L., Toth, B., Chandler, G., Ridley, S., Bugg, G., Molnar, A., Lochrie, D., Connor, J., Howe, D., Head, K., Wellstead, S., Mathers, A., Walker, L., Crawford, I., Davies, D., Garner, Z., Galloway, L., Davies, Y., Smith, C., Perkins, G., Geary, M., Walsh, F., Nagle, U., O’malley, L., Katakam, N., White, H., Tanton, E., Hamilton, R., Glanowski, H., Forde, E., Macdonald, C., McKay, L., Edoziern, L., Doran, P., Dillon, J., Taylor, C., Evans, P., Miller, V., Wayne, C., Tebbutt, J., Hendy, E., O’brien, P., Subair, S., Dent, H., Mallet, C., Quenby, S., Hillen, J., Young, P., Harrison, T., Wood, L., Arya, R., Roughley, L., Sorinola, O., Rogers, C., Phipps, J., Arndtz, B., Azzopardi, D., Chivers, Z., Cole, A., Parmar, M., Roberts, T., Sanders, J., Tuffnell, D., Ashby, D., Norman, J., Shennan, A., Spiby, H., Tin, W.: Computerised interpretation of fetal heart rate during labour (INFANT): a randomised controlled trial. The Lancet 389(10080), 1719–1729 (2017) https://doi.org/10.1016/S0140-6736(17)30568-8 Chudáček et al. [2014] Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. 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[2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141
- Chudáček, V., Spilka, J., Burša, M., Janků, P., Hruban, L., Huptych, M., Lhotská, L.: Open access intrapartum CTG database. BMC Pregnancy and Childbirth 14(1), 1–12 (2014) https://doi.org/10.1186/1471-2393-14-16/FIGURES/2 Georgieva et al. [2019] Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141
- Georgieva, A., Abry, P., Chudáček, V., Djurić, P.M., Frasch, M.G., Kok, R., Lear, C.A., Lemmens, S.N., Nunes, I., Papageorghiou, A.T., Quirk, G.J., Redman, C.W.G., Schifrin, B., Spilka, J., Ugwumadu, A., Vullings, R.: Computer-based intrapartum fetal monitoring and beyond: A review of the 2nd Workshop on Signal Processing and Monitoring in Labor (October 2017, Oxford, UK). Acta Obstetricia et Gynecologica Scandinavica 98(9), 1207–1217 (2019) https://doi.org/10.1111/AOGS.13639 [25] Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141
- Cardiotocography - UCI Machine Learning Repository. https://archive.ics.uci.edu/dataset/193/cardiotocography Brocklehurst et al. [2017] Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Brocklehurst, P., Field, D.J., Juszczak, E., Kenyon, S., Linsell, L., Newburn, M., Plachcinski, R., Quigley, M., Schroeder, L., Steer, P.: The INFANT trial. The Lancet 390(10089), 28 (2017) https://doi.org/10.1016/S0140-6736(17)31594-5 Hug et al. [2022] Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Hug, L., You, D., Blencowe, H., Mishra, A., Wang, Z., Fix, M.J., Wakefield, J., Moran, A.C., Gaigbe-Togbe, V., Suzuki, E., Blau, D.M., Cousens, S., Creanga, A., Croft, T., Hill, K., Joseph, K.S., Maswime, S., McClure, E.M., Pattinson, R., Pedersen, J., Smith, L.K., Zeitlin, J., Alkema, L.: Global, Regional, and National Estimates and Trends in Stillbirths from 2000 to 2019: A Systematic Assessment. Obstetrical and Gynecological Survey 77(2), 83–85 (2022) https://doi.org/10.1097/01.OGX.0000816512.11007.84 Georgieva et al. [2013] Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Georgieva, A., Payne, S.J., Moulden, M., Redman, C.W.G.: Artificial neural networks applied to fetal monitoring in labour. Neural Computing and Applications 22(1), 85–93 (2013) https://doi.org/10.1007/S00521-011-0743-Y/FIGURES/6 Petrozziello et al. [2019] Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. [2024] Lin, Z., Liu, X., Wang, N., Li, R., Liu, Q., Ma, J., Wang, L., Wang, Y., Hong, S.: Deep Learning with Information Fusion and Model Interpretation for Health Monitoring of Fetus based on Long-term Prenatal Electronic Fetal Heart Rate Monitoring Data (2024) Jones et al. [2024] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. 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[2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: A Performance Evaluation of Computerised Antepartum Fetal Heart Rate Monitoring: The Dawes-Redman Algorithm at Term (2024) Romagnoli et al. [2020] Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. 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[2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141
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[2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. 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Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141
- Petrozziello, A., Redman, C.W.G., Papageorghiou, A.T., Jordanov, I., Georgieva, A.: Multimodal Convolutional Neural Networks to Detect Fetal Compromise during Labor and Delivery. IEEE Access 7, 112026–112036 (2019) https://doi.org/10.1109/ACCESS.2019.2933368 Feng et al. [2023] Feng, J., Liang, J., Qiang, Z., Hao, Y., Li, X., Li, L., Chen, Q., Liu, G., Wei, H.: A hybrid stacked ensemble and Kernel SHAP-based model for intelligent cardiotocography classification and interpretability. BMC Medical Informatics and Decision Making 23(1), 1–12 (2023) https://doi.org/10.1186/S12911-023-02378-Y/TABLES/7 Spilka et al. [2016] Spilka, J., ChudáČek, V., Huptych, M., Leonarduzzi, R., Abry, P., Doret, M.: Intrapartum fetal heart rate classification: Cross-database evaluation. IFMBE Proceedings 57, 1193–1198 (2016) https://doi.org/10.1007/978-3-319-32703-7{_}232/CO Lin et al. 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[2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Romagnoli, S., Sbrollini, A., Burattini, L., Marcantoni, I., Morettini, M., Burattini, L.: Annotation dataset of the cardiotocographic recordings constituting the “CTU-CHB intra-partum CTG database”. Data in Brief 31, 105690 (2020) https://doi.org/10.1016/J.DIB.2020.105690 Sbrollini et al. [2017] Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Sbrollini, A., Agostinelli, A., Burattini, L., Morettini, M., Di Nardo, F., Fioretti, S., Burattini, L.: CTG Analyzer: A graphical user interface for cardiotocography. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1, 2606–2609 (2017) https://doi.org/10.1109/EMBC.2017.8037391 Jones et al. [2022] Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. Maternal-Fetal Medicine 4(2), 130–140 (2022) https://doi.org/10.1097/FM9.0000000000000141 Jones, G.D., Cooke, W.R., Vatish, M., Redman, C.W.G., Pan, Y.: Computerized Analysis of Antepartum Cardiotocography: A Review. 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