Epileptic Seizure Detection Based on Complexity Feature of EEG
Main Article Content
Abstract
Brain disorder characterized by seizure is a common disease among people in the world. Characterization of electroencephalogram (EEG) signals in terms of complexity can be used to identify neurological disorders. In this study, a non-linear epileptic seizure detection method based on multiscale entropy (MSE) has been employed to characterize the complexity of EEG signals. For this reason, the MSE method has been applied on Bonn dataset containing seizure and non-seizure EEG data and the corresponding results in terms of complexity have been obtained. Using statistical tests and support vector machine (SVM), the classification ability of the MSE method has been verified on Bonn dataset. Our results show that the MSE method is a viable approach to identifying epileptic seizure demonstrating a classification accuracy of 91.7%.
Downloads
Article Details
Submission of any work for publication in this journal would imply that the authors acknowledge that the work is their own and that they have taken all necessary permissions for all the materials used in their work.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
Authors permit us for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.