Sleep-Wake Classification using Statistical Features Extracted from Photoplethysmographic Signals

Sleep quality has a significant impact on human mental and physical health. Detecting sleep-wake stages is of paramount importance in the study of sleep. The gold standard method for sleep-wake stages classification is the multi-sensors based polysomnography (PSG) systems, which is normally recorded in clinical settings. The main drawback of PSG is the inconvenience to the subjects and can hamper the normal sleep. This paper describes an automated approach for classifying sleep-wake stages using finger-tip photoplethysmographic (PPG) signal. The proposed system used statistical features of PPG signal and supervised machine learning models including K-nearest neighbors (KNN) and support vector machine (SVM). The models are trained using 80% events (3486 sleep-wake events) from the dataset and the rest 20% events (872 sleep-wake events) are used for testing. On the test events, cubic KNN, weighted KNN, quadratic SVM and medium Gaussian SVM show 69.27%, 70.53%, 71.33% and 72.36% overall accuracy respectively for predicting the sleep and wake stages. This result advocates that the statistical features of PPG are capable of recognizing the changes in physiological states. The KNN and SVM classifier adopt the statistical features from PPG signal to differentiate between the wake and sleep stages.