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

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 parkinsonism. 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 parkinsonism.

Keywords: EMG; Parkinson’s disease; RBD; REM sleep behavior disorder; REM sleep without atonia; electromyography; parkinsonism; polysomnography; spectral power.

Figure 1

Schematic (CONSORT) overview of our patient population, exclusion criteria, and subsequent sub-analysis groups. Of the total n = 595 participants evaluated with in-lab polysomnography (PSG), we excluded n = 134 records (due to having <4 h of total sleep time, n = 76; <10 epochs of REM sleep, n = 45, or were otherwise incomplete, n = 13). Of these n = 461 participants, n = 164 and n = 275 were noted to be currently using antidepressant medications or were on CPAP/BiPAP during their PSG, respectively. Since antidepressant use and presence of untreated obstructive sleep apnea have both been associated with increased EMG tone during REM sleep, we wanted to examine these groups as separate subsets, given that these conditions might affect the accuracy of our algorithm.

Figure 2

Overall median second-by-second EMG relative spectral power around periods of no REM sleep without atonia events and periods with an REM sleep without atonia event.

Figure 3

Epoch-based HF:LF analysis: horizontal violin plots (non-truncated/halved) with corresponding box-whisker plots illustrating the spread across Group E1 (open plot; average HF:LF in all epochs of REM sleep without REM sleep without atonia), Group E2 (light shaded plot; average HF:LF in every epoch of REM sleep with exactly 1 REM sleep without atonia event), and Group E3 (heavy shaded plot; average HF:LF in every epoch of REM sleep with ≥2 REM sleep without atonia events). The heavy solid line corresponds to the median value with the 25th and 75th percentile indicated by the bracketed lines/box outline. Whiskers indicate the 5th and 95th percentiles.

Figure 4

Window-based HF:LF analysis: horizontal violin plots (non-truncated/halved) with corresponding box-whisker plots illustrating the HF:L:F spread across Group W1 (open plot; the HF:LF in every second of REM sleep with no REM sleep without atonia), and Group W2 (light shaded plot; the average HF:LF in the 3 s window around every REM sleep without atonia event). The heavy solid line corresponds to the median value with the 25th and 75th percentile indicated by the bracketed lines/box outline. Whiskers indicate the 5th and 95th percentiles.