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.
Author(s) Name:  Rahul Sharma, Ram Bilas Pachori, Pradip Sircar
Journal name:  Biomedical Signal Processing and Control
Publisher name:  Elsevier
Volume Information:  Volume 59, May 2020, 101921
Paper Link:   https://www.sciencedirect.com/science/article/abs/pii/S174680942030077X