Pair-Wise Matching of EEG Signals for Epileptic Identification via Convolutional Neural Network

Jian Lian,Yan Zhang,Rong Luo,Weikuan Jia,Chengjiang Li,Guoyong Han

doi:10.1109/access.2020.2976751

Abstract

Electroencephalogram (EEG) have been extensively analyzed to identify the characteristics of epileptic seizures in the literature. However, most of these studies focus on the properties of single channel EEG data while neglecting the association between signals from diverse channels. To bridge this gap, we propose an EEG instance matching-based epilepsy classification approach by introducing one convolutional neural network (CNN). First of all, each pair of EEG signals are exploited to form one 2 dimensional matrix, which could be used to reveal the interaction between them. Secondly, the generated matrices are fed into the proposed CNN that would discriminate the input representations. To evaluate the performance of the presented approach, the comparison experiments between the state-of-the-art techniques and our work are conducted on publicly available epilepsy EEG benchmark database. Experimental results indicate that the proposed algorithm could yield the performance with an average accuracy of 99.3%, average sensitivity of 99.5%, and average specificity 99.6%.

Highlights

Being a typical brain recording modality, Electroencephalogram (EEG) has been widely applied in the detection and identification of epileptic seizures
Gotman [6] proposed the first epileptic EEG recognition algorithm, in which the EEG signals were decomposed into elementary waves whose peak amplitude, duration, slope, and sharpness were simultaneously as the representations for epilepsy in EEG samples
Bearing the above-mentioned analysis in mind, we propose a convolutional neural network (CNN)-based pipeline for classifying the types of input EEG signals

Summary

Introduction

Being a typical brain recording modality, Electroencephalogram (EEG) has been widely applied in the detection and identification of epileptic seizures. Those electric fields from brain are captured by scalp EEG equipment that could provide an economical and non-invasive fashion. The precise interpretation of EEG data were implemented manually. Since it is a time-consuming and laborious task, the automated classification of EEG samples had become a buzzing field in current studies [1]–[5]. Gotman [6] proposed the first epileptic EEG recognition algorithm, in which the EEG signals were decomposed into elementary waves whose peak amplitude, duration, slope, and sharpness were simultaneously as the representations for epilepsy in EEG samples. Adeli et al [5], [7], [8] introduced the wavelet (WT) features including discrete Daubechies and harmonic

Methods

Results

Conclusion