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

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.

Posttraumatic stress disorder increases the odds of REM sleep behavior disorder and other parasomnias in Veterans with and without comorbid traumatic brain injury.

STUDY OBJECTIVES: To describe the crude prevalence of rapid eye movement (REM) sleep behavior disorder (RBD) following traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) in Veterans, given potential relationships between TBI, PTSD, RBD, and neurodegeneration. METHODS: Veterans (n = 394; 94% male; 54.4 ± 15.5 years of age) were prospectively/cross-sectionally recruited from the VA Portland Health Care System and completed in-lab video-polysomnography and questionnaires. TBI and PTSD were assessed via diagnostic screening and medical record review. Subjects were categorized into four groups after assessment of REM sleep without atonia (RSWA) and self-reported dream enactment: (1) "Normal," neither RSWA nor dream enactment, (2) "Other Parasomnia," dream enactment without RSWA, (3) "RSWA," isolated-RSWA without dream enactment, and (4) "RBD," RSWA with dream enactment. Crude prevalence, prevalence odds ratio, and prevalence rate for parasomnias across subjects with TBI and/or PTSD were assessed. RESULTS: Overall prevalence rates were 31%, 7%, and 9% for Other Parasomnia, RSWA, and RBD, respectively. The prevalence rate of RBD increased to 15% in PTSD subjects [age adjusted POR: 2.81 (1.17-4.66)] and to 21% in TBI + PTSD subjects [age adjusted POR: 3.43 (1.20-9.35)]. No subjects met all diagnostic criteria for trauma-associated sleep disorder (TASD), and no overt dream enactment was captured on video. CONCLUSIONS: The prevalence of RBD and related parasomnias is significantly higher in Veterans compared with the general population and is associated with PTSD and TBI + PTSD. Considering the association between idiopathic-RBD and synucleinopathy, it remains unclear whether RBD (and potentially TASD) associated with PTSD or TBI + PTSD similarly increases risk for long-term neurologic sequelae.