Validation of visually identified muscle potentials during human sleep using high frequency/low frequency spectral power ratios

Mo H. Modarres, Jonathan E. Elliott, Kristianna B. Weymann, Dennis Pleshakov, Donald L. Bliwise, Miranda M. Lim

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number55
JournalSensors
Volume22
Issue number1
DOIs
StatePublished - Jan 1 2022
Externally publishedYes

Keywords

  • EMG
  • Electromyography
  • Parkinsonism
  • Parkinson’s disease
  • Polysomnography
  • RBD
  • REM sleep behavior disorder
  • REM sleep without atonia
  • Spectral power

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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