Non-contact determination of sleep/wake state in residential environments by neural network learning of microwave radar and electroencephalogram–electrooculogram measurements

Abstract

This study developed and validated a non-contact, integrated sleep measurement method using microwave radar for daily sleep measurement in a house. In order to replicate the participant's normal sleep as much as possible, we employed a portable electroencephalogram–electrooculogram (EEG–EOG) recording system, which has the lowest burden and established reliability in the bedroom environment. A total of 38 nights of sleep for 15 participants were simultaneously recorded using the portable EEG–EOG recording system, actigraph, and microwave radar in the bedroom of typical houses. The sleep/wake state determination obtained by applying EEG–EOG data to a deep learning-based sleep staging program was used as the target variable. A sleep/wake state determination prediction model was obtained by learning body movement data from microwave radar using a neural network. The inter-method reliability was evaluated using sleep parameters, including sleep onset latency (SOL), wake after sleep onset (WASO), total sleep time (TST), and sleep efficiency (SE), by using paired t-test. Based on this prediction model, the average accuracy, sensitivity, specificity, F1-score*100, and Cohen's kappa*100 for all participants' sleep/wake state determination using microwave radar were 95.27%, 70.11%, 97.05%, 65.95, and 63.46, respectively. Compared with the portable EEG–EOG recording system, microwave radar showed no statistically significant differences in SOL, TST, and SE in evaluating sleep parameters. As the main conclusion, in the sleep/wake state determination, microwave radar showed performance comparable with or better than actigraph. It serves for continuous non-contact sleep measurement in residential environments.