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

Abstract

Objective 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. Method 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. Results 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. Conclusion 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. Significance Future research will optimize the model to categorize other types of epilepsies beyond BECTS and finally implement them in the hospital system.

Keywords:

• Rolandic seizure
• electroencephalogram
• empirical mode decomposition
• complex network
• convolutional neural network
• epilepsy
• BECTS

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Author contribution

Jialin Wang: Conceptualization, Methodology, Software. Tian Luo: Data curation, Writing Original draft preparation. Yuanfeng Zhou/Shuizhen Zhou: Visualization, Investigation. Chunhui Hu: Supervision. Peili Yao/Yanjiong Zhang: Software, Validation. Yi Wang: Writing-Reviewing and Editing.

Ethical approval

The study was performed with the Children's Hospital of Fudan University Ethics Board (No.2016 [117]).

Additional information

Funding

This article is funded by the Shanghai Municipal Science and Technology Major Project (Grant No. 2017SHZDZX01), Error! Hyperlink reference not valid. (Grant No. 2016YFC0904400), and the young scientist project of the MOE innovation platform.