Sleep/wake classification using cardiorespiratory features extracted from photoplethysmogram

Human sleep cyclically alternates between wakefulness and different sleep stages. There are various physiological changes that occur during wakefulness and sleep transitions. In particular, fluctuations occur in cardiorespiratory activity, mainly determined by the autonomic nervous system. The purpose of this study was to implement a multivariate logistic regression model to classify 30-second epochs of an overnight sleep dataset into awake and sleep states using the features extracted from the photoplethysmogram (PPG). The extracted features provided information about heart rate variability, respiratory activity, vascular tone and body movement. Overnight PPG signals were collected using a smartphone-based pulse oximeter, simultaneously with standard polysomnography from 160 children at the British Columbia Children's hospital. The sleep technician scored all wake/sleep epochs throughout the PSG study. We divided the dataset into training data, used to develop the model using LASSO, and test data, used to validate the model. The developed model was assessed epoch-by-epoch for each subject individually, andfor the complete test dataset. The performance of the model on the full test dataset showed a median accuracy of 77%, sensitivity of 80%, and specificity of 70%. Thus, providing a detailed epoch-by-epoch analysis with at-home pulse oximetry alone is feasible with accuracy, sensitivity and specificity values above 70%. However, the performance of the model might decrease when analyzing subjects with a high number epochs of wakefulness.