Observations on muscle activity in REM sleep behavior disorder assessed with a semi-automated scoring algorithm. Issue 3 (March 2018)

• Record Type: Journal Article
• Authors: Jeppesen, Jesper; Otto, Marit; Frederiksen, Yoon; Hansen, Allan K.; Fedorova, Tatyana D.; Knudsen, Karoline; Nahimi, Adjmal; Brooks, David J.; Borghammer, Per; Sommerauer, Michael
• Is Part Of: Clinical neurophysiology. Volume 129:Issue 3(2018:Mar.)
• Page Start: 541
• Page End: 547
• Publication Date: 2018-03
• Subjects: REM sleep without atonia -- REM sleep behavior disorder -- Parkinson's disease -- Automated computer algorithm; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2017.12.029
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Visual rating of muscle atonia severity in REM sleep behavior disorder is demanding. Our computer algorithm quantifies muscle activity higly correlated to visual ratings. Automated EMG analysis provides a useful tool for quantifying muscle activity in RBD. Abstract: Objectives: Rapid eye movement (REM) sleep behavior disorder (RBD) is defined by dream enactment due to a failure of normal muscle atonia. Visual assessment of this muscle activity is time consuming and rater-dependent. Methods: An EMG computer algorithm for scoring 'tonic', 'phasic' and 'any' submental muscle activity during REM sleep was evaluated compared with human visual ratings. Subsequently, 52 subjects were analyzed with the algorithm. Duration and maximal amplitude of muscle activity, and self-awareness of RBD symptoms were assessed. Results: The computer algorithm showed high congruency with human ratings and all subjects with RBD were correctly identified by excess of submental muscle activity, when artifacts were removed before analysis. Subjects with RBD exhibited prolonged bouts of 'phasic' muscle activity with high amplitude. Self-awareness of RBD symptoms correlated with amount of REM sleep without atonia. Conclusions: Our proposed algorithm was able to detect and rate REM sleep without atonia allowing identification of RBD. Increased duration and amplitude of muscle activity bouts were characteristics of RBD. Quantification of REM sleep without atonia represents a marker of RBD severity. Significance: Our EMG computer algorithm can support a diagnosis of RBD while the quantification of altered muscle activity provides a measure of its severity.