Diykh, Mohammed and Abdulla, Shahab 
ORCID: https://orcid.org/0000-0002-1193-6969 and Oudah, Atheer Y. and Marhoon, Haydar Abdulameer and Siuly, Siuly
(2021)
A novel Alcoholic EEG signals Classification Approach Based on AdaBoost k-means Coupled with Statistical Model.
In: 10th International Conference on Health Information Science (HIS 2021), 25 Oct - 28 Oct 2021, Melbourne, Australia.
Abstract
Identification of alcoholism is an important task because it affects the operation of the brain. Alcohol consumption, particularly heavier drinking is identified as an essential factor to develop health issues, such as high blood pressure, immune disorders, and heart diseases. To support health professionals in diagnosis disorders related with alcoholism with a high rate of accuracy, there is an urgent demand to develop an automated expert systems for identification of alcoholism. In this study, an expert system is proposed to identify alcoholism from multi-channel EEG signals. EEG signals are partitioned into small epochs, with each epoch is further divided into sub-segments. A covariance matrix method with its eigenvalues is utilised to extract representative features from each sub-segment. To select most relevant features, a statistic approach named Kolmogorov–Smirnov test is adopted to select the final features set. Finally, in the classification part, a robust algorithm called AdaBoost k-means (AB-k-means) is designed to classify EEG features into two categories alcoholic and non-alcoholic EEG segments. The results in this study show that the proposed model is more efficient than the previous models, and it yielded a high classification rate of 99%. In comparison with well-known classification algorithms such as K-nearest k-means and SVM on the same databases, our proposed model showed a promising result compared with the others. Our findings showed that the proposed model has a potential to implement in automated alcoholism detection systems to be used by experts to provide an accurate and reliable decisions related to alcoholism.
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| Item Type: | Conference or Workshop Item (Commonwealth Reporting Category E) (Paper) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Faculty/School / Institute/Centre: | Current - USQ College (8 Jun 2020 -) |
| Faculty/School / Institute/Centre: | Current - USQ College (8 Jun 2020 -) |
| Date Deposited: | 13 Feb 2022 23:11 |
| Last Modified: | 09 Jun 2022 01:18 |
| Uncontrolled Keywords: | alcoholism, EEG, AdaBoost k-means, covariance matrix, Kolmogorov–Smirnov |
| Fields of Research (2008): | 11 Medical and Health Sciences > 1199 Other Medical and Health Sciences > 119999 Medical and Health Sciences not elsewhere classified |
| Fields of Research (2020): | 42 HEALTH SCIENCES > 4299 Other health sciences > 429999 Other health sciences not elsewhere classified |
| Socio-Economic Objectives (2008): | C Society > 92 Health > 9299 Other Health > 929999 Health not elsewhere classified E Expanding Knowledge > 97 Expanding Knowledge > 970111 Expanding Knowledge in the Medical and Health Sciences |
| Socio-Economic Objectives (2020): | 20 HEALTH > 2099 Other health > 209999 Other health not elsewhere classified |
| Identification Number or DOI: | https://doi.org/10.1007/978-3-030-90885-0_8 |
| URI: | http://eprints.usq.edu.au/id/eprint/44781 |
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