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

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

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Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, 75 Amherst St., Cambridge, MA 02139, U.S.A.


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.

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