Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain.

Muhammad Tariq Sadiq,Bilal Masood,Mohammed K A Kaabar,Mahdieh Ghazvini,Jingwei Too,Zuhaib Nishtar,Ateeq Ur Rehman,Hesam Akbari,Nastaran Hamedi,Cosimo Ieracitano

doi:10.1155/2021/6283900

Abstract

For drug resistance patients, removal of a portion of the brain as a cause of epileptic seizures is a surgical remedy. However, before surgery, the detailed analysis of the epilepsy localization area is an essential and logical step. The Electroencephalogram (EEG) signals from these areas are distinct and are referred to as focal, while the EEG signals from other normal areas are known as nonfocal. The visual inspection of multiple channels for detecting the focal EEG signal is time-consuming and prone to human error. To address this challenge, we propose a novel method based on differential operator and Tunable Q-factor wavelet transform (TQWT) to distinguish the focal and nonfocal signals. For this purpose, first, the EEG signal was differenced and then decomposed by TQWT. Second, several entropy-based features were derived from the TQWT subbands. Third, the efficacy of the six binary feature selection algorithms, binary bat algorithm (BBA), binary differential evolution (BDE) algorithm, firefly algorithm (FA), genetic algorithm (GA), grey wolf optimization (GWO), and particle swarm optimization (PSO), was evaluated. In the end, the selected features were fed to several machine learning and neural network classifiers. We observed that the PSO with neural networks provides an effective solution for the application of focal EEG signal detection. The proposed framework resulted in an average classification accuracy of 97.68%, a sensitivity of 97.26%, and a specificity of 98.11% in a tenfold cross-validation strategy, which is higher than the state of the art used in the public Bern-Barcelona EEG database.

Highlights

Seizure symptoms might affect any part of the body, Journal of Healthcare Engineering electrical disorders related to all of them occur in the brain. e biggest challenge in neuroscience is understanding the behavior of epilepsy and its effect on the brain
Traditional binary particle swarm optimization (PSO) and most of its variants use different probability functions to cope with discrete optimization problems. e input parameters of binary PSO are the number of iterations (T), the number of particles (N), cognitive learning factor (c1), the social learning factor (c2), the maximum bound on inertia weight, the minimum bound on inertia weight, the maximum velocity (VMax), and the total features in a particle
We showed that the performance of heuristic algorithms in the reduction of feature vector arrays is better than the KruskalWallis statistical (KWS) test, features selected by some heuristic algorithms resulted in lesser classification ACC

Summary

Introduction

Almost all of the previously proposed machine learning methods in focal and nonfocal signal classification application use p value for selecting significant features [6, 11, 13, 14, 16, 17, 19, 22, 23, 25, 26, 31, 33,34,35], in such a way that features with p values less than 0.05 were significant and could be used as an input to classifiers. To the best of the authors’ knowledge, the entropies of TQWT subbands of differenced EEG signals and BBA, BDE, FA, GA, GWO, and PSO algorithms as feature selection, as well as FFNN, CFNN, GRNN, and RNN classifiers, have not been previously employed for the focal and nonfocal EEG signals classification.

Proposed Method

Feature Selection

Results

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