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Epileptic Seizure Detection from EEG Signals Using Multiband Features with Feedforward Neural Network

Electroencephalography (EEG) is considered as a potential tool for diagnosis of epilepsy in clinical applications. Epileptic seizures occur irregularly and unpredictably. Its automatic detection in EEG recordings is highly demanding. In this work, multiband features are used to detect seizure with feedforward neural network (FfNN). The EEG signal is segmented into epochs of short duration and each epoch is decomposed into a number of subbands using discrete wavelet transform (DWT). Three features namely ellipse area of second-order difference plot, coefficient of variation and fluctuation index are computed from each subband signal. The features obtained from all subbands are combined to construct the feature vector. The FfNN is trained using the derived feature vector and seizure detection is performed with test data. The experiment is performed with publicly available dataset to evaluate the performance of the proposed method. The experimental results show the superiority of this method compared to the recently developed algorithms.