Aarhus University Seal / Aarhus Universitets segl

Detection of convulsive seizures using surface electromyography

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperReviewResearchpeer-review

DOI

  • Sándor Beniczky
  • Isa Conradsen, FORCE Technology
  • ,
  • Peter Wolf, Department of Clinical Medicine, Neurological Service, Federal University of Santa Catarina, Florianopolis, SC, Brazil, Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund

Bilateral (generalized) tonic-clonic seizures (TCS) increase the risk of sudden unexpected death in epilepsy (SUDEP), especially when patients are unattended. In sleep, TCS often remain unnoticed, which can result in suboptimal treatment decisions. There is a need for automated detection of these major epileptic seizures, using wearable devices. Quantitative surface electromyography (EMG) changes are specific for TCS and characterized by a dynamic evolution of low- and high-frequency signal components. Algorithms targeting increase in high-frequency EMG signals constitute biomarkers of TCS; they can be used both for seizure detection and for differentiating TCS from convulsive nonepileptic seizures. Two large-scale, blinded, prospective studies demonstrated the accuracy of wearable EMG devices for detecting TCS with high sensitivity (76%-100%). The rate of false alarms (0.7-2.5/24 h) needs further improvement. This article summarizes the pathophysiology of muscle activation during convulsive seizures and reviews the published evidence on the accuracy of EMG-based seizure detection.

Original languageEnglish
JournalEpilepsia
Volume59 Suppl 1
Pages (from-to)23-29
Number of pages7
ISSN0013-9580
DOIs
Publication statusPublished - Jun 2018

    Research areas

  • Algorithms, Electromyography/instrumentation, Humans, Muscle, Skeletal/physiopathology, Seizures/diagnosis, Seizure detection, biomarkers, electromyography, tonic–clonic seizures, tonic

See relations at Aarhus University Citationformats

ID: 141431820