Efficient novel network and index for alcoholism detection from EEGs

Muhammad Tariq Sadiq; Siuly Siuly; Ahmad Almogren; Yan Li; Paul Wen

doi:10.1007/s13755-023-00227-w

Efficient novel network and index for alcoholism detection from EEGs

Health Inf Sci Syst. 2023 Jun 17;11(1):27. doi: 10.1007/s13755-023-00227-w. eCollection 2023 Dec.

Authors

Muhammad Tariq Sadiq¹, Siuly Siuly², Ahmad Almogren³, Yan Li⁴, Paul Wen⁵

Affiliations

¹ Advanced Engineering Centre, School of Architecture, Technology and Engineering, University of Brighton, Brighton, BN2 4AT UK.
² Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, 3011 Australia.
³ Department of Computer Science, College of Computer and Information Sciences, King Saud University, 11633 Riyadh, Saudi Arabia.
⁴ School of Mathematics Physics and Computing, University of Southern Queensland, Toowoomba Campus, Toowoomba, 4350 Australia.
⁵ School of Engineering, University of Southern Queensland, Toowoomba Campus, Toowoomba, 4350 Australia.

Abstract

Background: Alcoholism is a catastrophic condition that causes brain damage as well as neurological, social, and behavioral difficulties.

Limitations: This illness is often assessed using the Cut down, Annoyed, Guilty, and Eye-opener examination technique, which assesses the intensity of an alcohol problem. This technique is protracted, arduous, error-prone, and errant.

Method: As a result, the intention of this paper is to design a cutting-edge system for automatically identifying alcoholism utilizing electroencephalography (EEG) signals, that can alleviate these problems and aid practitioners and investigators. First, we investigate the feasibility of using the Fast Walsh-Hadamard transform of EEG signals to explore the unpredictable essence and variability of EEG indicators in the suggested framework. Second, thirty-six linear and nonlinear features for deciphering the dynamic pattern of healthy and alcoholic EEG signals are discovered. Subsequently, we suggested a strategy for selecting powerful features. Finally, nineteen machine learning algorithms and five neural network classifiers are used to assess the overall performance of selected attributes.

Results: The extensive experiments show that the suggested method provides the best classification efficiency, with 97.5% accuracy, 96.7% sensitivity, and 98.3% specificity for the features chosen using the correlation-based FS approach with Recurrent Neural Networks. With recently introduced matrix determinant features, a classification accuracy of 93.3% is also attained. Moreover, we developed a novel index that uses clinically meaningful features to differentiate between healthy and alcoholic categories with a unique integer. This index can assist health care workers, commercial companies, and design engineers in developing a real-time system with 100% classification results for the computerized framework.

Keywords: Alcoholism; Automatic identification; Classification; Electroencephalography (EEG); Features.

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