Why the study?
Conventional sleep monitoring relies on multiple physiological sensors that are uncomfortable for long-term use and require expert intervention.
Does a CNN derived from PPG signals accurately classify sleep stages compared to existing models?
Does a CNN derived from PPG signals accurately classify sleep stages compared to existing models?
A novel CNN model using only PPG signals can accurately classify sleep stages, offering a potentially more comfortable alternative to conventional multi-sensor sleep monitoring.
May enable comfortable long-term sleep monitoring; leaves open need for clinical validation before adoption.
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.
No takes yet. Share an insight, caveat, or question.
Habib et al. (2022) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: