Detection of seizures in EEG using subband nonlinear parameters and genetic algorithm

Detection of seizures in EEG can be challenging because of myogenic artifacts and might be time-consuming. In this study, we propose a method using subband nonlinear parameters and genetic algorithm for automatic seizure detection in EEG. In the experiment, the discrete wavelet transform was used to decompose EEG into five subband components. Nonlinear parameters were extracted and employed as the features to train the support vector machine with linear kernel function (SVML) and radial basis function kernel function (SVMRBF) classifiers. A genetic algorithm (GA) was used for selecting the effective feature subset. The seizure detection sensitivities of the SVML and the SVMRBF with GA are 90.8% and 94.0%, respectively. The sensitivity of SVMRBF increases to 95.8% by using GA for weight adjustment. Moreover, the proposed method is capable of discriminating the interictal EEG of epileptic subjects from the normal EEG, which is considered difficult, yet crucial, in clinical services.

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