Skip to main navigation Skip to search Skip to main content

Seizure Onset Detection based on one sEMG channel

  • Isa Conradsen
  • , Sandor Beniczky
  • , Karsten Hoppe
  • , Peter Wolf
  • , Thomas Sams
  • , Helge Bjarup Dissing Sørensen
    • University of Southern Denmark
    • DELTA - a Part of FORCE Technology
    • Danish Epilepsy Center

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

    Abstract

    We present a new method to detect seizure onsets of tonic-clonic epileptic seizures based on surface electromyography (sEMG) data. The proposed method is generic and based on a single channel making it ideal for a small detection or monitoring device. The sEMG signal is high-pass filtered with a Butterworth filter with a cut-off frequency of 150 Hz. The number of zero-crossings with a hysteresis of ±50μV is the only feature extracted. The number of counts in a window of 1 second and the number of windows to make a detection is tested with a leave-one-out method. On 6 patients the method performs with a sensitivity of 100%, a median latency of 7.6 seconds and a median false detection rate of 0.04/h.
    Original languageEnglish
    Title of host publicationProceedings of the 33rd Annual International Conference of the IEEE EMBS
    PublisherIEEE
    Publication date2011
    Pages7715-7718
    ISBN (Print)978-1-4244-4122-8
    DOIs
    Publication statusPublished - 2011
    Event2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Boston, United States
    Duration: 30 Aug 20113 Sept 2011
    Conference number: 33
    https://ieeexplore.ieee.org/xpl/conhome/6067544/proceeding

    Conference

    Conference2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
    Number33
    Country/TerritoryUnited States
    CityBoston
    Period30/08/201103/09/2011
    Internet address

    Keywords

    • Sensitivity
    • Conferences
    • Training
    • Epilepsy
    • Muscles
    • Feature extraction
    • Monitoring

    Fingerprint

    Dive into the research topics of 'Seizure Onset Detection based on one sEMG channel'. Together they form a unique fingerprint.

    Cite this