Su H Hwang, Yu J Lee, Do U Jeong, Kwang S Park1. 1. Kwang Suk Park, Room # 714, Basic Science Building, Seoul National University College of Medicine, 103, Daehakro, Jongno-gu, Seoul, Republic of Korea, E-mail: pks@bmsil.snu.ac.kr.
Abstract
OBJECTIVES: The aim of this study is to establish a sleep monitoring method that can classify sleep into four stages in an unconstrained manner using a polyvinylidene fluoride (PVDF) sensor for continuous and accurate estimation of sleep stages. METHODS: The study participants consisted of 12 normal subjects and 13 obstructive sleep apnea (OSA) patients. The physiological signals of the subjects were unconstrainedly measured using the PVDF sensor during polysomnography. The respiration and body movement signals were extracted from the PVDF data. Rapid eye movement (REM) sleep was estimated based on the average rate and variability of the respiratory signal. Wakefulness was detected based on the body movement signal. Variability of the respiratory rate was chosen as an indicator for slow-wave sleep (SWS) detection. Sleep was divided into four stages (wake, light, SWS, and REM) based on the detection results. RESULTS: The performance of the method was assessed by comparing the results with a manual scoring by a sleep physician. In an epoch-by-epoch analysis, the method classified the sleep stages with an average accuracy of 70.9 % and kappa statistics of 0.48. No significant differences were observed in the detection performance between the normal and OSA groups. CONCLUSIONS: The developed system and methods can be applied to a home sleep monitoring system.
OBJECTIVES: The aim of this study is to establish a sleep monitoring method that can classify sleep into four stages in an unconstrained manner using a polyvinylidene fluoride (PVDF) sensor for continuous and accurate estimation of sleep stages. METHODS: The study participants consisted of 12 normal subjects and 13 obstructive sleep apnea (OSA) patients. The physiological signals of the subjects were unconstrainedly measured using the PVDF sensor during polysomnography. The respiration and body movement signals were extracted from the PVDF data. Rapid eye movement (REM) sleep was estimated based on the average rate and variability of the respiratory signal. Wakefulness was detected based on the body movement signal. Variability of the respiratory rate was chosen as an indicator for slow-wave sleep (SWS) detection. Sleep was divided into four stages (wake, light, SWS, and REM) based on the detection results. RESULTS: The performance of the method was assessed by comparing the results with a manual scoring by a sleep physician. In an epoch-by-epoch analysis, the method classified the sleep stages with an average accuracy of 70.9 % and kappa statistics of 0.48. No significant differences were observed in the detection performance between the normal and OSA groups. CONCLUSIONS: The developed system and methods can be applied to a home sleep monitoring system.
Authors: Mustafa Radha; Pedro Fonseca; Arnaud Moreau; Marco Ross; Andreas Cerny; Peter Anderer; Xi Long; Ronald M Aarts Journal: Sci Rep Date: 2019-10-02 Impact factor: 4.379