Title
Seizure detection in EEG signals using support vector machines
Document Type
Conference Paper
RIS ID
60292
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.
COinS
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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.