A Novel Alcoholic EEG Signals Classification Approach Based on AdaBoost k-means Coupled with Statistical Model
Paper
Paper/Presentation Title | A Novel Alcoholic EEG Signals Classification Approach Based on AdaBoost k-means Coupled with Statistical Model |
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Presentation Type | Paper |
Authors | Diykh, Mohammed (Author), Abdulla, Shahab (Author), Oudah, Atheer Y. (Author), Marhoon, Haydar Abdulameer (Author) and Siuly, Siuly (Author) |
Editors | Siuly, Siuly, Wang, Hua, Chen, Lu, Guo, Yanhui and Xing, Chunxiao |
Journal or Proceedings Title | Proceedings of the 10th International Conference on Health Information Science (HIS 2021) |
Journal Citation | 13079, pp. 82-92 |
Number of Pages | 11 |
Year | 2021 |
Place of Publication | Cham, Switzerland |
ISBN | 9783030908843 |
9783030908850 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-90885-0_8 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-030-90885-0_8 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-030-90885-0 |
Conference/Event | 10th International Conference on Health Information Science (HIS 2021) |
Event Details | 10th International Conference on Health Information Science (HIS 2021) Parent International Conference on Health Information Science (HIS) Delivery In person Event Date 25 to end of 28 Oct 2021 Event Location 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. |
Keywords | alcoholism, EEG, AdaBoost k-means, covariance matrix, Kolmogorov–Smirnov |
ANZSRC Field of Research 2020 | 429999. Other health sciences not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | University of Thi-Qar, Iraq |
USQ College | |
Al-Ayen University, Iraq | |
Victoria University | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q6w8q/a-novel-alcoholic-eeg-signals-classification-approach-based-on-adaboost-k-means-coupled-with-statistical-model
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