A1 Refereed original research article in a scientific journal
PPG-Based Sleep Stage Classification Using Pulse Wave Feature Fusion and Explainable AI
Authors: Smarandache, Florentin; Akula, Satyasri; Alzahrani, Saleh I.; Arslan, Farrukh; Ijaz, Amir
Publisher: Dionysios Pylarinos
Publication year: 2025
Journal: Engineering, Technology and Applied Science Research
Volume: 15
Issue: 5
First page : 27640
Last page: 27645
ISSN: 2241-4487
eISSN: 1792-8036
DOI: https://doi.org/10.48084/etasr.13077
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Open Access publication channel
Web address : https://doi.org/10.48084/etasr.13077
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/508830374
Self-archived copy's licence: CC BY
Self-archived copy's version: Publisher`s PDF
Sleep monitoring plays a crucial role in understanding and managing various health conditions, including sleep disorders, cardiovascular diseases, and mental health. Traditional sleep monitoring methods rely on Electroencephalography (EEG) and Polysomnography (PSG) in clinical settings. However, these methods are expensive, difficult to administer, and unsuitable for home-based monitoring. In recent years, photoplethysmogram (PPG) has emerged as a promising noninvasive technology that is widely used in wearable devices and holds great potential for sleep assessment. Yet, most current sleep monitoring methods rely on deep learning models, which are inherently "black-box" and challenging in the clinical decision-making process. In this paper, we propose an explainable random forest model for sleep stage classification using pulse wave feature fusion. Our method employs statistical, temporal, and nonlinear dynamical features extracted from the PPG pulse wave associated with sleep patterns. Additionally, we investigate the digital biomarkers of sleep and PPG using SHAP (SHapley Additive exPlanations) methods to enhance interpretability. The proposed approach demonstrates competitive performance, achieving an overall accuracy of 82.56% in two-stage (sleep and wake) classification, 77.79% in three-stage (wake, NREM, REM) classification, and 69.20% in four-stage (wake, light sleep, deep sleep, REM) classification. The results highlight the potential of PPG-based wearable devices in sleep monitoring, offering a feasible solution for home-based assessments with clinical applicability.
Downloadable publication This is an electronic reprint of the original article. |  |
