Sadiq, Muhammad Tariq and Siuly, Siuly and Almogren, Ahmad and Li, Yan and Wen, Paul (2023) Efficient novel network and index for alcoholism detection from EEGs. Health Information Science and Systems, 11 (1). 27-. DOI https://doi.org/10.1007/s13755-023-00227-w
Sadiq, Muhammad Tariq and Siuly, Siuly and Almogren, Ahmad and Li, Yan and Wen, Paul (2023) Efficient novel network and index for alcoholism detection from EEGs. Health Information Science and Systems, 11 (1). 27-. DOI https://doi.org/10.1007/s13755-023-00227-w
Sadiq, Muhammad Tariq and Siuly, Siuly and Almogren, Ahmad and Li, Yan and Wen, Paul (2023) Efficient novel network and index for alcoholism detection from EEGs. Health Information Science and Systems, 11 (1). 27-. DOI https://doi.org/10.1007/s13755-023-00227-w
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.
Item Type: | Article |
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Uncontrolled Keywords: | Alcoholism; Automatic identification; Classification; Electroencephalography (EEG); Features |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 01 Aug 2024 14:59 |
Last Modified: | 01 Aug 2024 15:03 |
URI: | http://repository.essex.ac.uk/id/eprint/38000 |