Classification of EEG Signals Using Hilbert-Huang Transform-Based Deep Neural Networks

Abstract

Epilepsy is one of the most common neurologic disease. Electroencephalogram (EEG) contains physiologic and pathological information related human nervous system. EEG signals used in this study are obtained from Bonn University, Department of Epileptology EEG database. Original database has five subsets (A, B, C, D, E). Data have been reorganized into three groups which are healthy (AB), interictal (CD) and ictal EEG signals. Furthermore, in order to examine effect of signal length on classification performance, three different lengths are used. Hilbert-Huang transform is applied to the signals and they are represented as image files. Then, generated images are fed into deep neural networks with five different structures for classification. Accuracy is calculated for all cases to asses performance of proposed method. it is clear that successful results could be obtained using Hilbert-Huang transform along with deep learning networks.