Abstract
The detection of epileptic EEG signals is a challenging task due to bulky size and nonstationary nature of the data. From a pattern recognition point of view, one key problem is how to represent the large amount of recorded EEG signals for further analysis such as classification.This chapter introduces a new classification algorithm combining a simple random sampling (SRS) technique and a least square support vector machine (LS-SVM) to identify epilptic seizure from two-class EEG signals.
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