Data from: Ictal quantitative surface electromyography correlates with postictal EEG suppression

Objective: To test the hypothesis that neurophysiological biomarkers of muscle activation during convulsive seizures reveal seizure severity, and to determine whether automatically computed surface electromyography (EMG) parameters during seizures can predict postictal generalized EEG suppression (PGES) indicating increased risk for sudden unexpected death in epilepsy (SUDEP). Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures (GTCS). Our goal was to use automated EMG measurements for seizure-characterization and risk-assessment. Methods: Quantitative parameters were automatically computed from surface EMG recorded during convulsive seizures, from deltoid and brachial biceps muscles, in patients admitted to long-term video-EEG monitoring. Parameters evaluated were: the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, as well as the dynamics of their evolution (slope). We compared them with the duration of the PGES. Results: We found significant correlations between automatically computed EMG parameters and the duration of PGES (p<0.001). Stepwise multiple regression analysis identified as independent predictors, in deltoid muscle the duration of the clonic phase, and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. An algorithm constructed from these parameters identified seizures at increased risk (PGES ≥20s) with an accuracy of 87%. Conclusions: Automatically computed ictal EMG parameters correlate with PGES and may identify seizures at high risk. Classification of Evidence: This study provides Class II evidence that quantitative ictal EMG parameters are biomarkers for high-risk convulsive seizures.