Title

Seizure detection in EEG signals using support vector machines

RIS ID

60292

Publication Details

Seng, C., Demirli, R., Khuon, L. & Bolger, D. (2012). Seizure detection in EEG signals using support vector machines. 2012 38th Annual Northeast Northeast Bioengineering Conference (NEBEC) (pp. 231-232). USA: IEEE.

Abstract

A linear Support Vector Machine (SVM) classifier is designed to detect and classify seizures in EEG signals based on a few simple features such as mean, variance, dominant frequency, and the mean power spectrum. The SVM classifier is tested on a benchmark EEG database. Using a combination of these features, classification rates up to 98% were achieved. The proposed classifier that utilizes a few simple features is computationally efficient to be deployed in a real-time seizure monitoring system.

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Link to publisher version (DOI)

http://dx.doi.org/10.1109/NEBC.2012.6207048