SOMAS – an open-source software for the analysis of muscle activity during sleep

Abstract

Study objectives

While several algorithms exist for analyzing muscle activity during sleep, none provides information on both muscle tone and movements as open-source software. We aimed to overcome this limitation by developing SOMAS (Sleep Open-source Muscle activity Analysis System).

Methods

SOMAS processes European Data Format+ (EDF+) files with wake-sleep state and candidate leg movement annotations without online data sharing, quantifies muscle tone using the atonia index and the distribution of normalized electromyography values (DNE), and calculates leg movement indices based on the 2016 World Association of Sleep Medicine criteria. To demonstrate that SOMAS achieves its intended purpose, we analyzed recordings from eight patients with isolated REM sleep behavior disorder (iRBD), five with restless legs syndrome (RLS), seven with sleep breathing disorders, and five controls. SOMAS-derived atonia index and leg movement indices were compared with those from Hypnolab, a non-open access software. Additionally, SOMAS-derived indices were used to differentiate patients with iRBD or with RLS from other patients and/or controls.

Results

SOMAS-derived atonia index and leg movement indices strongly correlated with Hypnolab results (Spearman coefficients >0.97) with minimal bias. The DNE and atonia index in REM sleep effectively differentiated patients with iRBD from other patients and controls (AUC 0.89–1.00). The periodic leg movement and periodicity indices differentiated patients with RLS from controls (AUC 0.71–0.75).

Conclusions

SOMAS reliably quantifies muscle tone and movements during sleep from EDF+ files using open-source algorithms, with the potential of enhancing reproducibility and collaboration in research on sleep-related movement disorders.