Research Area: Machine Learning
The epileptic seizure is a transient and abnormal discharge of nerve cells in the brain that leads to a chronic disease of brain dysfunction. There are various features-based seizures classification algorithms listed in the literature. But, there is no standardized set of attributes that can perfectly capture the relevant information regarding the signal dynamics. In this paper, a computationally-fast seizure classification algorithm is presented. The obtained results through the proposed algorithm are consistent and repeatable. This paper describes an automated seizures classification technique using the nonlinear higher-order statistics and deep neural network algorithms. The sparse autoencoder based deep neural network is used to extract the essential structural details from the third-order cumulant coefficients matrix. The proposed algorithm achieves a reliable classification accuracy for both categories, i.e., binary classes and three-classes of electroencephalogram (EEG) signals with the softmax classifier. The proposed study is simulated on the publicly-available Bonn university EEG database. The achieved results show the effectiveness of the proposed algorithm for seizures classification.
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Author(s) Name: Rahul Sharma, Ram Bilas Pachori, Pradip Sircar
Journal name: Biomedical Signal Processing and Control
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Publisher name: Elsevier
DOI: 10.1016/j.bspc.2020.101921
Volume Information: Volume 59, May 2020, 101921
