Seizure detection in EEG signals using support vector machines

Authors

Cher Hau Seng, University of WollongongFollow
Ramazan Demirli, Villanova UniversityFollow
Lunal Khuon, Villanova University
Donovan Bolger, Villanova University

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.