Validation of Visually Identified Muscle Potentials during Human Sleep Using High Frequency/Low Frequency Spectral Power Ratios

• Type of Material: Article
• Authors: Mo H Modarres, VA Bedford Health Care System; Jonathan E Elliott, VA Portland Health Care System; Kristianna B Weymann, Oregon Health & Science University; Dennis Pleshakov, Oregon Health & Science University; Donald Bliwise, Emory University; Miranda M Lim, VA Portland Health Care System
• Language: English
• Date: 2022-01-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2021 by the authors.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/s22010055
• Title of Journal or Parent Work: SENSORS
• Volume: 22
• Issue: 1
• Grant/Funding Information: The data in this work was supported with resources and the use of facilities at the VA Portland Health Care System, VA RRD Merit Award #I01 RX002846 to M.H.M. and M.M.L.; VA CSRD Merit Award #I01 CX002022 to M.M.L.; VA RRD Career Development Award #1K2 RX002947 to J.E.E.; NIH NIA R34 AG056639 to J.E.E., D.L.B., and M.M.L.; The interpretations and conclusions expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs, the National Institute of Health, or the United States government.
• Keywords: RISK; Parkinson's disease; QUANTIFICATION; Engineering; Chemistry, Analytical; spectral power; DISTURBANCES; DEMENTIA; Engineering, Electrical & Electronic; Physical Sciences; Technology; RBD; Chemistry; polysomnography; Instruments & Instrumentation; TRAUMATIC BRAIN-INJURY; EMG; electromyography; REM sleep without atonia; REM-SLEEP; Science & Technology; BRUXISM; EMG ACTIVITY; parkinsonism; REM sleep behavior disorder; BEHAVIOR DISORDER
• Research Categories: Health Sciences, Mental Health; Health Sciences, Medicine and Surgery

Abstract

Surface electromyography (EMG), typically recorded from muscle groups such as the mentalis (chin/mentum) and anterior tibialis (lower leg/crus), is often performed in human subjects undergoing overnight polysomnography. Such signals have great importance, not only in aiding in the definitions of normal sleep stages, but also in defining certain disease states with abnormal EMG activity during rapid eye movement (REM) sleep, e.g., REM sleep behavior disorder and parkin-sonism. Gold standard approaches to evaluation of such EMG signals in the clinical realm are typically qualitative, and therefore burdensome and subject to individual interpretation. We originally developed a digitized, signal processing method using the ratio of high frequency to low frequency spectral power and validated this method against expert human scorer interpretation of transient muscle activation of the EMG signal. Herein, we further refine and validate our initial approach, applying this to EMG activity across 1,618,842 s of polysomnography recorded REM sleep acquired from 461 human participants. These data demonstrate a significant association between visual interpretation and the spectrally processed signals, indicating a highly accurate approach to detecting and quantifying abnormally high levels of EMG activity during REM sleep. Accordingly, our automated approach to EMG quantification during human sleep recording is practical, feasible, and may provide a much‐needed clinical tool for the screening of REM sleep behavior disorder and parkin-sonism.