Detection of alcoholic EEG signals based on whole brain connectivity and convolution neural networks
Article
Article Title | Detection of alcoholic EEG signals based on whole brain connectivity and convolution neural networks |
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ERA Journal ID | 3391 |
Article Category | Article |
Authors | Shen, Mingkan (Author), Wen, Peng (Author), Song, Bo (Author) and Li, Yan (Author) |
Journal Title | Biomedical Signal Processing and Control |
Journal Citation | 79 (Part 2), pp. 1-8 |
Article Number | 104242 |
Number of Pages | 8 |
Year | 2023 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 1746-8094 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.bspc.2022.104242 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S1746809422006966 |
Abstract | Alcoholism is a common complex brain disorder caused by excessive drinking of alcohol and severely affected the basic function of the brain. This paper investigates classification of the alcoholic electroencephalogram (EEG) signals through whole brain connectivity analysis and deep learning methods. The whole brain connectivity analysis is proposed and implemented using mutual information algorithm. Continuous Wavelet transform was applied to extract time–frequency domain information in each selected frequency bands from EEG signal. The 2D and 3D convolutional neural networks (CNN) were used to classify the alcoholic subjects and health control subjects. UCI Alcoholic EEG dataset is employed to evaluate the proposed method, a 96.25 ± 3.11 % accuracy, 0.9806 ± 0.0163 F1-score result in 3D-CNN model was obtained via leaving-one out training method of all the testing subjects. |
Keywords | EEG, Alcoholism, Continuous wavelet transform, Mutual information, Whole brain connectivity, 3D-CNN |
Related Output | |
Is part of | Real-time epilepsy seizure detection and brain connectivity analysis using electroencephalogram |
ANZSRC Field of Research 2020 | 400399. Biomedical engineering not elsewhere classified |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
This article is part of a UniSQ Thesis by publication. See Related Output. | |
Byline Affiliations | School of Mechanical and Electrical Engineering |
School of Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7v5y/detection-of-alcoholic-eeg-signals-based-on-whole-brain-connectivity-and-convolution-neural-networks
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