Automation of classification of sleep stages and estimation of sleep efficiency using actigraphy - Kim, Hyejin; Kim, Dongsin; Oh, Junhyoung (Frontiers in Public Health, 2023)
Sleep is a fundamental and essential physiological process for recovering physiological function. Sleep disturbance or deprivation has been known to be a causative factor for many physiological and psychological disorders. Evaluation of sleep, therefore, has importance for diagnosing or monitoring of those disorders. Although PSG (polysomnography) has been the gold standard for assessing sleep quality and classifying sleep stages, PSG has various limitations for common uses. In substitution for PSG, there has been vigorous research using actigraphy. For classifying sleep stages automatically, we propose machine learning models with HRV (heart rate variability)-related features and acceleration features, which were processed from the actigraphy (Maxim band) data. Those classification results were transformed into a binary classification for estimating sleep efficiency. With 30 subjects, we conducted PSG and they slept overnight with wrist-type actigraphy. We assessed the performance of our proposed machine learning models. With HRV-related features and raw features of actigraphy, the accuracy was 0.98 for classifying sleep stages into 5 stages: awake (W), REM (R), Sleep N1 (S1), Sleep N2 (S2), Sleep N3 (S3). When the acceleration features and the raw features were included for modeling, the accuracy was 0.79. Our machine learning model for estimation of sleep efficiency showed an accuracy of 0.86.