[Discrimination of Chin Electromyography in REM Sleep Behavior Disorder Using Deep Learning].

Journal: Nihon eiseigaku zasshi. Japanese journal of hygiene

PMID: 35314576

Abstract

OBJECTIVE: The confirmation of abnormal behavior during video monitoring in polysomnography (PSG) and the frequency of rapid eye movement (REM) sleep without atonia (RWA) during REM sleep based on physiological indicators are essential diagnostic criteria for the diagnosis of REM sleep behavior disorder (RBD). However, no clear criteria have been established for the determination of the tonic and phasic activities of RWA. In this study, we investigated an RWA decision program that simulates visual inspection by clinical laboratory technicians.

Authors

Fumiya Kinoshita

Graduate School of Engineering, Toyama Prefectural University.
Meiho Nakayama

Department of Otolaryngology & Good Sleep Center, Nagoya City University.
Hiroki Takada

Graduate School of Engineering, University of Fukui.

Keywords

Aged Chin Deep Learning Electromyography Female Humans Male REM Sleep Behavior Disorder Sleep, REM

External Resources

View on PubMed Access via DOI PubMed (35314576)

[Discrimination of Chin Electromyography in REM Sleep Behavior Disorder Using Deep Learning].

Abstract

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