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Detection of epileptic seizures in stereo-EEG using frequency-weighted energy

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3 Author(s)
Rajeev Yadav ; Department of Electrical and Computer Engineering, Concordia University 1455, de Maisonneuve Blvd. West, Montreal, Quebec, H3G 1M8, CANADA ; Rajeev Agarwal ; M. N. S. Swamy

This paper proposes a new algorithm for seizure detection based on the evolution-like characteristics of a seizure. Most of the existing algorithms for automatic detection of the epileptic seizures in electroencephalograms (EEG) rely upon some pre-defined/patient-tunable detection threshold to classify the data as normal or abnormal. In this paper, we present a method for seizure detection in stereoencephalograms (SEEG) using frequency-weighted energy. The method does not require a threshold or any a priori information about the seizure for its detection. The method is gradient-based and any activity that exceeds the minimum duration satisfying our criteria is considered as a potential seizure activity. The performance of the algorithm is evaluated on 100 hours of single channel SEEG data obtained from five different patients. An overall sensitivity of 96.6% and a false detection rate of 0.21/h is obtained on the complete data.

Published in:

2007 50th Midwest Symposium on Circuits and Systems

Date of Conference:

5-8 Aug. 2007