Sadiq, Muhammad Tariq and Akbar, Hesam and Siuly, Siuly and Li, Yan Li 
ORCID: https://orcid.org/0000-0002-4694-4926 and Wen, Peng (Paul) ORCID: https://orcid.org/0000-0003-0939-9145
(2022)
Alcoholic EEG signals recognition based on phase space dynamic and geometrical features.
Chaos, Solitons & Fractals, 158:112036.
pp. 1-12.
ISSN 0960-0779
Abstract
Alcoholism is a severe disorder that leads to brain problems and associated cognitive, emotional and behavioral impairments. This disorder is typically diagnosed by a questionnaire technique known as CAGE that measures the severity of a drinking problem. This is a time-consuming, onerous, error-prone, and biased method. Hence, this article aims to establish a novel framework for automatic detecting alcoholism using electroencephalogram (EEG) signals, which can mitigate these issues and help clinicians and researchers. In the proposed framework, firstly, we explore the phase space dynamic of EEG signals for visualizing the chaotic nature and complexity of EEG signals. Secondly, we discover thirty-four graphical features for decoding the chaotic behavior of normal and alcoholic EEG signals. After that, we investigate thirteen feature selection in combination with eleven machine learning and neural network classifiers to select the best combination for the development of an efficient framework. The experimental results reveal that the proposed method achieves the highest classification performance involving 99.16% accuracy, 100% sensitivity and 98.36% specificity for the twenty-three features selected by Henry gas solubility optimization with feedforward neural network (FFNN). The proposed system provides a new visual biomarker for alcoholic detection. In addition, we developed two new indexes using clinically relevant features to distinguish normal and alcoholic classes with a single number. These indexes can help medical teams, commercial users as well as product developers to develop a real-time system.
|
Statistics for this ePrint Item |
| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Files associated with this item cannot be displayed due to copyright restrictions. |
| Faculty/School / Institute/Centre: | Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -) |
| Faculty/School / Institute/Centre: | Current – Faculty of Health, Engineering and Sciences - School of Engineering (1 Jan 2022 -) |
| Date Deposited: | 13 May 2022 00:11 |
| Last Modified: | 13 May 2022 00:12 |
| Uncontrolled Keywords: | Electroencephalography, Normal and alcoholic EEG signals, Phase space dynamic, Graphical features, Feature selection, Novel indexes, Classification |
| Fields of Research (2020): | 40 ENGINEERING > 4003 Biomedical engineering > 400304 Biomedical imaging 46 INFORMATION AND COMPUTING SCIENCES > 4601 Applied computing > 460102 Applications in health 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460308 Pattern recognition |
| Socio-Economic Objectives (2020): | 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence 20 HEALTH > 2099 Other health > 209999 Other health not elsewhere classified 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing |
| Identification Number or DOI: | https://doi.org/10.1016/j.chaos.2022.112036 |
| URI: | http://eprints.usq.edu.au/id/eprint/48466 |
Actions (login required)
 |
Archive Repository Staff Only |