Discrimination of Focal and Non-Focal Seizures From EEG Signals Using Sliding Mode Singular Spectrum Analysis

Epilepsy is a neurological disorder, and it is diagnosed using electroencephalogram (EEG) signal. The discrimination of focal and non-focal categories of EEG signals is the primary task to locate epilepsy affected regions in the brain. In this paper, a novel approach to classify the focal and non-focal types of EEG signals is proposed. This approach is based on the decomposition of the EEG signal into reconstruction components (RCs) using sliding mode-singular spectrum analysis (SM-SSA). A total of five RCs are extracted from each EEG signal using SM-SSA. Then, a classifier is designed by combining the sparse-autoencoder (SAE) hidden layer, and radial basis function neural network (RBFN). Each RC obtained from the SM-SSA of EEG signal, and the SAE based RBFN (SAE-RBFN) classifier are used to classify the focal and non-focal types of EEG signals. The performance of the proposed approach is assessed using a publicly available database. The experimental results demonstrate that the third RC coupled with SAE-RBFN classifier produces an average accuracy, average sensitivity and average specificity values of 99.11%, 98.52%, and 99.70%, respectively using 10-fold cross-validation. The proposed approach is compared with existing methods for the discrimination of focal and non-focal EEG signals.