Automatic Seizure Detection Based on Support Vector Machines with Genetic Algorithms
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.
KeywordsSupport Vector Machine Recognition Accuracy Feature Subset Support Vector Machine Model Channel Selection
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