Exploiting feature selection and neural network techniques for identification of focal and nonfocal EEG signals in TQWT domain

Sadiq, Muhammad Tariq and Akbari, Hesam and Rehman, Ateeq Ur and Nishtar, Zuhaib and Masood, Bilal and Ghazvini, Mahdieh and Too, Jingwei and Hamedi, Nastaran and Kaabar, Mohammed K. A. (2021) Exploiting feature selection and neural network techniques for identification of focal and nonfocal EEG signals in TQWT domain. Journal of Healthcare Engineering, 2021. ISSN 2040-2295, DOI https://doi.org/10.1155/2021/6283900.

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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.

Item Type: Article
Uncontrolled Keywords: Q wavelet transform; Cardiac sound signals; Filter-bank; Automated detection; Classification; Diagnosis; Discrimination; Localization; Index
Subjects: R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions: Faculty of Science > Institute of Mathematical Sciences
Depositing User: Ms Zaharah Ramly
Date Deposited: 13 Jun 2022 02:56
Last Modified: 13 Jun 2022 02:56
URI: http://eprints.um.edu.my/id/eprint/34262

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