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Automated Processing of Single-Channel Surface Electromyography From Generalized Tonic–Clonic Seizures to Inform Semiology

Cardenas, Damon P.*; Halford, Jonathan J.; Whitmire, Luke E.*; Morgan, Lola C.; Lie, Octavian V.; Jetter, Gina M.§; Cavazos, Jose E.*,‡

doi: 10.1097/WNP.0000000000000618
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Purpose: Advances in surface electromyography (sEMG) monitoring allow for long-term data collection in a natural environment, giving objective information that may identify risk of sudden unexpected death in epilepsy and guide clinical decision-making. Generalized tonic–clonic seizure semiology, namely motor tonic and clonic phase duration, may be an important factor in determining the level of seizure control and risk of sudden unexpected death in epilepsy. This study demonstrates a quantitative analysis of sEMG collected with a dedicated wearable device.

Methods: During routine monitoring, 23 generalized tonic–clonic seizures from 19 subjects were simultaneously recorded with video-EEG and sEMG. A continuous wavelet-transform was used to determine the frequency components of sEMG recorded during generalized tonic–clonic seizures. An automated process, incorporating a variant of cross-validation, was created to identify ideal frequencies and magnitude ranges for tonic and clonic phases and determine phase durations. Phase durations determined using sEMG analysis were compared with phase durations determined by independent epileptologists' review of video-EEG.

Results: Cross-validation revealed that the optimal frequency bands for tonic and clonic phases are 150 to 270 Hz and 12 to 70 Hz, respectively. The average difference in phase duration calculated using the two methods for tonic and clonic phases and total seizure duration were −0.42 ± 4.94, −5.12 ± 9.68, and −5.11 ± 11.33 seconds, respectively (results presented are TsEMG − TvEEG, μ ± σ).

Conclusions: The automated processing of sEMG presented here accurately identified durations of tonic, clonic, and total motor durations of generalized tonic–clonic seizures similar to durations identified by epileptologists' review of video-EEG.

*Brain Sentinel Inc, San Antonio, Texas, U.S.A.;

Medical University of South Carolina, Charleston, South Carolina, U.S.A.;

UT Health San Antonio, San Antonio, Texas, U.S.A.; and

§UT Health East Texas, Tyler, Texas, U.S.A.

Address correspondence and reprint requests to Damon P. Cardenas, PhD, Brain Sentinel Inc, 8023 Vantage Drive, San Antonio, TX 78261, U.S.A.; e-mail: Damon.Cardenas@brainsentinel.com.

D. P. Cardenas and L. E. Whitmire are full-time employees of Brain Sentinel Inc. J. E. Cavazos is a co-founder and holds stock in Brain Sentinel Inc. D. P. Cardenas, J. J. Halford, L. E. Whitmire, and J. E. Cavazos are authors on a provisional patent filed that contains background information on the algorithm mentioned in this work. The name of the filed patent is “Systems and Methods for Characterization of Seizures” (US20170296083A1). This patent has been followed up by a nonprovisional patent of the same title that has yet to be issued. Brain Sentinel (first affiliation) provided all funding for this work.

D. P. Cardenas and J. J. Halford are co-first authors.

Presented as a poster at the Annual Meeting of American Epilepsy Society, New Orleans, December 3, 2018.

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal.

© 2019 by the American Clinical Neurophysiology Society