Non-Contact Sleep Stage Estimation Using Wireless Millimetre-Wave Sensor

Abstract

Insufficient sleep quality has significant physical and mental health impacts on humans. However, measuring sleep quality requires a Polysomnography (PSG) test at a clinical site, which requires specialised knowledge. This study used a wireless millimetre-wave, non-contact biophysical information detection sensor to estimate sleep depth. This sensor can obtain body movements and respiratory rates from millimetre-wave fluctuations. We calculated several parameters from the respiration and body movement data obtained from these sensors and applied machine learning to create a model for estimating sleep depth. In addition, we applied the sleep stage probability obtained in advance from the sleep stages of all the experimental subjects using simple PSG as one of the sleep stage estimation parameters. The actual sleep stage was obtained using a simple PSG as a reference for machine learning. The experimental subjects were 15 healthy adults, and measurements were taken over 1–3 nights. Because some of the wireless millimetre-wave sensors did not work correctly and the first night of measurement did not provide normal sleep owing to the first-night effect, we excluded some data. Finally, nine sets of data were used for training. Sleep depth was classified into four stages: waking (W), rem (R), light (L), and deep (D). The sensitivities of this machine-learning model for each sleep depth were 51.1% (W), 27.6% (R), 81.0% (L), and 47.8% (D), and the correct response rates were 73.7% (W), 53.3% (R), 59.8% (L), and 64.3% (D). The overall accuracy is 60.5%. In the future, we will implement hidden Markov state transition probabilities in the state probabilities. In addition, sensors can detect heartbeats to improve the accuracy of sleep-depth estimation.

Keywords: Sleep stage estimation, Machine Learning, Respiratory movement, Body movement

DOI: 10.54941/ahfe1004388