Detecting rapid eye movement sleep using a single EEG signal channel. (30th November 2017)

• Record Type: Journal Article
• Authors: Lin, Nan-Hung; Hsu, Chung-Yao; Luo, Yuxi; Nagurka, Mark L.; Sung, Jia-Li; Hong, Chih-Yuan; Yen, Chen-Wen
• Is Part Of: Expert systems with applications. Volume 87(2017)
• Page Start: 220
• Page End: 227
• Publication Date: 2017-11-30
• Subjects: Rapid eye movement sleep -- Electroencephalography -- Automatic sleep staging -- Machine learning; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.06.017
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: REM sleep is detected using one channel electroencephalography signal. Special attention is given to the problem of interpersonal EEG signal differences. The REM sleep ratio and sleep apnea severity are studied as meaningful parameters. The approach was tested with data from 947 overnight polysomnography studies. Abstract: Sleep stage scoring is generally determined in a polysomnographic (PSG) study where technologists use electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG) signals to determine the sleep stages. Such a process is time consuming and labor intensive. To reduce the workload and to improve the sleep stage scoring performance of sleep experts, this paper introduces an intelligent rapid eye movement (REM) sleep detection method that requires only a single EEG channel. The proposed approach distinguishes itself from previous automatic sleep staging methods by introducing two sets of auxiliary features to help resolve the difficulties caused by interpersonal EEG signal differences. In addition to adopting conventional time and frequency domain features, two empirical rules are introduced to enhance REM detection performance based on sleep being a continuous process. The approach was tested with 779, 661 epochs obtained from 947 overnight PSG studies. The REM sleep detection results show a kappa coefficient at 0.752, an accuracy level of 0.930, a sensitivity score of 0.814, and a positive predictive value of 0.775. The results also show that the performance of the approach varies with the ratio of REM sleep and the severity of sleep apnea of the subjects. The experimental results also show that it is possible to improve the performance of an automatic sleep staging method by tailoring it to subgroups of persons that have similar sleep architecture and clinical characteristics.