Convulsive seizure detection using a wrist-worn electrodermal activity and accelerometry biosensor

Epilepsia. 2012 May;53(5):e93-7. doi: 10.1111/j.1528-1167.2012.03444.x. Epub 2012 Mar 20.


The special requirements for a seizure detector suitable for everyday use in terms of cost, comfort, and social acceptance call for alternatives to electroencephalography (EEG)-based methods. Therefore, we developed an algorithm for automatic detection of generalized tonic-clonic (GTC) seizures based on sympathetically mediated electrodermal activity (EDA) and accelerometry measured using a novel wrist-worn biosensor. The problem of GTC seizure detection was posed as a supervised learning task in which the goal was to classify 10-s epochs as a seizure or nonseizure event based on 19 extracted features from EDA and accelerometry recordings using a Support Vector Machine. Performance was evaluated using a double cross-validation method. The new seizure detection algorithm was tested on >4,213 h of recordings from 80 patients and detected 15 (94%) of 16 of the GTC seizures from seven patients with 130 false alarms (0.74 per 24 h). This algorithm can potentially provide a convulsive seizure alarm system for caregivers and objective quantification of seizure frequency.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Biosensing Techniques / methods*
  • Child
  • Child, Preschool
  • Electroencephalography / methods
  • Female
  • Galvanic Skin Response / physiology*
  • Humans
  • Kinetocardiography / methods*
  • Male
  • Seizures / diagnosis*
  • Time Factors
  • Wrist / innervation*