EEG-based classification of normal and seizure types using relaxed local neighbour difference pattern and artificial neural network

摘要

Diagnosis of epileptic seizure types is vital for the neurologists to comprehend the cortical connectivity of the brain, and to initiate the apt treatment for the epileptic subjects at the earliest. However, the conventional method of manually analysing the EEG signals, for seizure diagnosis and its types identification is onerous and it is error-prone. Hence, a novel relaxed local neighbour difference pattern (RLNDiP) technique integrated with artificial neural network is introduced in this article for robust and accurate automatic classification of normal(N), generalized(G), and focal(F) EEG signals. The proposed unified framework consists of two phases. Firstly, the EEG signals are pre-processed and transformed into RLNDiP domain, using the proposed method. The histogram features are computed to perform the classification task. Secondly, the most prominent features are selected using the Kruskal–Wallis test and given into ANN network for discrimination. To find out the best number of hidden layers and hidden neurons of ANN, the experiments with 5-fold cross validation technique is conducted with the train set. Finally, with the best values ANN network is trained employing the entire train set and evaluated on test set. Proposed methodology is validated using the Karunya Institute of Technology and Sciences (KITS) database consisting of 86 EEG signals in each group. The proposed method attained an overall classification accuracy of 100% with train set, and 95.83% with test set. The results encourage the plausible widening of proposed methodology to real time ambience for assisting the neurologists in making efficient diagnosis of epileptic seizure types.