Comprehensive Analysis of Machine Learning Models for Five-Class Sleep Stage Classification Using PPG Signals

Accurate classification of sleep stages is essential for diagnosing sleep disorders and understanding sleep quality. While polysomnography (PSG) remains the gold standard, its invasiveness and cost hinder large-scale deployment. Photoplethysmography (PPG) offers a non-invasive and wearable alternative, yet most prior work either simplifies staging into fewer than five classes or relies on deep learning approaches with high computational demands. This study systematically evaluates traditional machine learning models for five-class sleep stage classification using PPG data from the MESA Sleep Dataset. A subset of 48 participants was analyzed, with signals segmented into 3-minute windows to enable robust extraction of both PPG-derived and inter-beat interval (IBI) features. Models including Random Forest, k-Nearest Neighbors, and XGBoost were benchmarked, demonstrating that combining PPG and IBI features substantially improves performance. Random Forest achieved the best results, with an accuracy of 72.2% and Cohen's Kappa of 0.65, comparable to state-of-the-art deep learning methods while requiring fewer resources and offering greater interpretability. These findings highlight the potential of feature-based, computationally efficient models for five-stage sleep classification, supporting scalable and clinically relevant applications of wearable PPG monitoring.