Photoplethysmographic-based automated sleep-wake classification using a support vector machine.

Abstract

OBJECTIVE: Sleep quality has a significant impact on human mental and physical health. The detection of sleep-wake states is thus of paramount importance in the study of sleep. The gold standard method for sleep-wake classification is multi-sensor-based polysomnography (PSG) which is normally recorded in a clinical setting. The main drawbacks of PSG are the inconvenience to the subjects, the impact of discomfort on normal sleep cycles, and its requirement for experts' interpretation. In contrast, we aim to design an automated approach for sleep-wake classification using a wearable fingertip photoplethysmographic (PPG) signal. APPROACH: Time domain features are extracted from PPG and PPG-base..