Wearable Sleep-Stage Classification: A Systematic Analysis of Signal Modalities, Representations, and Multimodal Fusion

Accurate sleep staging is essential for characterizing sleep architecture, yet conventional polysomnography limits scalable and long-term sleep monitoring. Wearable sensors offer a practical alternative, but their performance depends on the physiological modalities used and how the signals are represented. This study investigates the effects of signal modality, feature representation, and sleep-stage granularity on subject-independent wearable sleep staging. Using DREAMT, we compare handcrafted features, short-time Fourier transform (STFT) representations, and their fusion across three-, four-, and five-class tasks. We further evaluate optical pulse signal-based classification using the larger MESA Sleep cohort. STFT improved class-balanced performance over handcrafted features, while BVP was the strongest individual STFT modality, particularly for REM. Hybrid fusion achieved Macro-F1 scores of 0.6476, 0.5108, and 0.4464 for the three-, four-, and five-class tasks, respectively. More complex fusion strategies did not improve Macro-F1 over simple concatenation. Overall, the results show that effective signal representation and complementary physiological information are more important than simply increasing the number of modalities or model complexity.