The nature of statistical learning theory
The nature of statistical learning theory
Epileptic seizure detection using dynamic wavelet network
Expert Systems with Applications: An International Journal
Automatic recognition of Alzheimer's disease using genetic algorithms and neural network
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartII
Clustering technique-based least square support vector machine for EEG signal classification
Computer Methods and Programs in Biomedicine
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The electroencephalogram (EEG) machine is the most influential tool in the diagnosis of epilepsy, which is one of the most common neurological disorders. In this paper, a new seizure detection approach, which combined the genetic algorithm (GA) and the support vector machine (SVM), is proposed to improve visual inspection of EEG recordings. Genetic operations are utilized to optimize the performance of SVM classifier, which includes three aspects: feature subset selection, channel subset selection and parameter optimization of SVM. These optimization operations are performed simultaneously during the training process. The epileptic EEG data acquired from hospital are divided into two parts of training set and testing set. The results from the test on EEG data show that the method may more effectively recognize the spike and sharp transients from the EEG recording of epileptic patients than those without using optimal operations.