EMD-WOG-2DCNN based EEG signal processing for Rolandic seizure classification.

Approximately 65 million people have epilepsy around the world. Recognition of epilepsy types is the basis to determine the treatment method and predict the prognosis in epilepsy patients. Childhood benign epilepsy with centrotemporal spikes (BECTS) or benign Rolandic epilepsy is the most common focal epilepsy in children, accounting for 15-20% of childhood epilepsies. These EEG patterns of individuals usually predict good treatment responses and prognosis. Until now, the interpretation of EEG still depends entirely on experienced neurologists, which may be a lengthy and tedious task. In this article, we proposed a novel machine learning model that efficiently distinguished Rolandic seizures from normal EEG signals. The proposed machine learning model processes the identification procedure in the following order (1) creating preliminary EEG features using signal empirical mode decomposition, (2) applying weighted overlook graph (WOG) to represent the decomposed EMD of IMF, and (3) classifying the results through a two Dimensional Convolutional Neural Network (2DCNN). The performance of our classification model is compared with other representative machine learning models. The model offered in this article gains an accuracy performance exceeding 97.6% in the Rolandic dataset, which is higher than other classification models. The effect of the model on the Bonn public dataset is also comparable to existing methods and even performs better in some subsets. The purpose of this study is to introduce the most common childhood benign epilepsy type and propose a model that meets the real clinical needs to distinguish this Rolandic EEG pattern from normal signals accurately. Future research will optimize the model to categorize other types of epilepsies beyond BECTS and finally implement them in the hospital system.