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PPG-to-ECG Signal Translation for Continuous Atrial Fibrillation Detection via Attention-based Deep State-Space Modeling (2309.15375v4)
Published 27 Sep 2023 in cs.LG
Abstract: Photoplethysmography (PPG) is a cost-effective and non-invasive technique that utilizes optical methods to measure cardiac physiology. PPG has become increasingly popular in health monitoring and is used in various commercial and clinical wearable devices. Compared to electrocardiography (ECG), PPG does not provide substantial clinical diagnostic value, despite the strong correlation between the two. Here, we propose a subject-independent attention-based deep state-space model (ADSSM) to translate PPG signals to corresponding ECG waveforms. The model is not only robust to noise but also data-efficient by incorporating probabilistic prior knowledge. To evaluate our approach, 55 subjects' data from the MIMIC-III database were used in their original form, and then modified with noise, mimicking real-world scenarios. Our approach was proven effective as evidenced by the PR-AUC of 0.986 achieved when inputting the translated ECG signals into an existing atrial fibrillation (AFib) detector. ADSSM enables the integration of ECG's extensive knowledge base and PPG's continuous measurement for early diagnosis of cardiovascular disease.
- J. Allen, “Photoplethysmography and its application in clinical physiological measurement,” Physiol Meas., vol. 28, no. 3, pp. 1–39, 2007.
- sudden, “Sudden death in young people: Heart problems often blamed,” Retrieved on September 2020, https://www.mayoclinic.org/diseases-conditions/sudden-cardiac-arrest/in-depth/sudden-death/art-20047571.
- “The risk factors and prevention of cardiovascular disease: the importance of electrocardiogram in the diagnosis and treatment of acute coronary syndrome,” Therapeutics and clinical risk management, vol. 12, pp. 1223, 2016.
- “How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management,” Cardiovascular Research, vol. 117, no. 7, pp. 1700–1717, 2021.
- “Utility of the photoplethysmogram in circulatory monitoring,” Anesthesiology: The Journal of the American Society of Anesthesiologists, vol. 108, no. 5, pp. 950–958, 2008.
- “Photoecg: Photoplethysmographyto estimate ecg parameters,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014, pp. 4404–4408.
- “Ecg reconstruction via ppg: A pilot study,” in 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). IEEE, 2019, pp. 1–4.
- “Cross-domain joint dictionary learning for ecg reconstruction from ppg,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020, pp. 936–940.
- “P2e-wgan: Ecg waveform synthesis from ppg with conditional wasserstein generative adversarial networks,” in Proceedings of the 36th Annual ACM Symposium on Applied Computing, 2021, pp. 1030–1036.
- “Cardiogan: Attentive generative adversarial network with dual discriminators for synthesis of ecg from ppg,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2021, vol. 35, pp. 488–496.
- “Reconstructing qrs complex from ppg by transformed attentional neural networks,” IEEE Sensors Journal, vol. 20, no. 20, pp. 12374–12383, 2020.
- “Stint: selective transmission for low-energy physiological monitoring,” in Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design, 2020, pp. 115–120.
- “Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review,” Computers in biology and medicine, vol. 122, pp. 103801, 2020.
- “Deep kalman filters,” arXiv preprint arXiv:1511.05121, 2015.
- “Structured inference networks for nonlinear state space models,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2017, vol. 31.
- “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473, 2014.
- “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114, 2013.
- “Learning structured output representation using deep conditional generative models,” Advances in neural information processing systems, vol. 28, 2015.
- “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555, 2014.
- “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014.
- “Mimic-iii waveform database matched subset,” MIMIC-III Waveform Database Matched Subset v1. 0, 2020.
- “Mimic-iii, a freely accessible critical care database,” Scientific data, vol. 3, no. 1, pp. 1–9, 2016.
- “Robust reconstruction of electrocardiogram using photoplethysmography: A subject-based model,” Frontiers in Physiology, p. 645, 2022.
- “Heartpy: A novel heart rate algorithm for the analysis of noisy signals,” Transportation research part F: traffic psychology and behaviour, vol. 66, pp. 368–378, 2019.
- “Analysing noisy driver physiology real-time using off-the-shelf sensors: Heart rate analysis software from the taking the fast lane project,” Journal of Open Research Software, vol. 7, no. 1, 2019.
- “A comparison of photoplethysmography and ecg recording to analyse heart rate variability in healthy subjects,” Journal of medical engineering & technology, vol. 33, no. 8, pp. 634–641, 2009.
- “Atrial fibrillation detection from raw photoplethysmography waveforms: A deep learning application,” Heart rhythm O2, vol. 1, no. 1, pp. 3–9, 2020.
- “The need for uncertainty quantification in machine-assisted medical decision making,” Nature Machine Intelligence, vol. 1, no. 1, pp. 20–23, 2019.
- “Mina: multilevel knowledge-guided attention for modeling electrocardiography signals,” arXiv preprint arXiv:1905.11333, 2019.
- Khuong Vo (7 papers)
- Mostafa El-Khamy (45 papers)
- Yoojin Choi (16 papers)