Paperdoi:10.1109/TBME.2022.3219863
Performance of a Convolutional Neural Network Derived From PPG Signal in Classifying Sleep Stages
IEEE Transactions on Biomedical Engineering
TL;DR
End-to-end CNN on PPG for 2-class (Wake–Sleep), 3-class (Wake–NREM–REM), and 4-class (Wake–Light–Deep–REM) staging. Reported average accuracies 94.4%, 94.2%, and 92.9% respectively, claimed to outperform prior classical and DL baselines in their evaluation setting.
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Abstract
Automatic sleep stage classification is vital for evaluating the quality of sleep. Conventionally, sleep is monitored using multiple physiological sensors that are uncomfortable for long-term monitoring and require expert intervention. In this study, we propose an automatic technique for multi-stage sleep classification using photoplethysmographic (PPG) signal. We have proposed a convolutional neural network (CNN) that learns directly from the PPG signal and classifies multiple sleep stages. We developed models for two- (Wake-Sleep), three- (Wake-NREM-REM) and four- (Wake-Light sleep-Deep sleep-REM) stages of sleep classification. Our proposed approach shows an average classification accuracy of 94.4%, 94.2%, and 92.9% for two, three, and four stages, respectively. Experimental results show that the proposed CNN model outperforms existing state-of-the-art models (classical and deep learning) in the literature.
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APA
Habib, A., Motin, M. A., Penzel, T., Palaniswami, M., Yearwood, J., & Karmakar, C. (2023). Performance of a Convolutional Neural Network Derived From PPG Signal in Classifying Sleep Stages. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2022.3219863
BibTeX
@article{habib2023performance,
title = {{Performance of a Convolutional Neural Network Derived From PPG Signal in Classifying Sleep Stages}},
author = {Habib, Ahsan and Motin, Mohammod Abdul and Penzel, Thomas and Palaniswami, Marimuthu and Yearwood, John and Karmakar, Chandan},
year = {2023},
journal = {IEEE Transactions on Biomedical Engineering},
doi = {10.1109/TBME.2022.3219863}
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