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Related Experiment Video

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Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
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Ictal quantitative surface electromyography correlates with postictal EEG suppression.

Anca A Arbune1, Isa Conradsen1, Damon P Cardenas1

  • 1From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark.

Neurology
|May 14, 2020
PubMed
Summary
This summary is machine-generated.

Quantitative surface electromyography (EMG) parameters during convulsive seizures can predict prolonged postictal generalized EEG suppression (PGES), a risk factor for sudden unexpected death in epilepsy. This technology aids in assessing seizure severity and risk.

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Epilepsy Research

Background:

  • Wearable surface electromyography (EMG) devices are validated for automated detection of generalized tonic-clonic seizures.
  • Assessing seizure severity and predicting risks like sudden unexpected death in epilepsy (SUDEP) remain critical clinical challenges.

Purpose of the Study:

  • To investigate neurophysiologic biomarkers of muscle activation during convulsive seizures.
  • To determine if surface EMG parameters can predict postictal generalized EEG suppression (PGES), a marker for SUDEP risk.

Main Methods:

  • Quantitative surface EMG parameters (tonic/clonic phase durations, burst/silent period dynamics) were analyzed from deltoid and biceps muscles during seizures.
  • These EMG parameters were correlated with the duration of PGES in patients undergoing long-term video-EEG monitoring.

Main Results:

  • Significant correlations were found between quantitative surface EMG parameters and PGES duration (p < 0.001).
  • Specific EMG parameters (clonic phase duration, tonic-clonic phase duration, average silent period, slopes) independently predicted PGES.
  • An EMG-based algorithm achieved 85% accuracy in identifying seizures with increased risk (PGES ≥20 seconds).

Conclusions:

  • Ictal quantitative surface EMG parameters correlate with PGES.
  • Surface EMG measurements can potentially identify seizures associated with a high risk of SUDEP.