Diagnosis of Tobacco Addiction using Medical Signal: An EEG-based Time-Frequency Domain Analysis Using Machine Learning
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86306307. Available under License Creative Commons Attribution 4.0. |
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Description
Addiction such as tobacco smoking affects the human brain and thus causes significant changes in the brainwaves. The changes in brain wave due to smoking can be identified by focusing on changes in electroencephalogram pattern, extracting different time-frequency domain features. In this aspect, a laboratory-based study has been presented in this paper, for assessing the brain signal changes due to the tobacco addiction. Four classifier models, namely, Logistic Regression (LR), K- Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest Classifier (RFC) were trained and tested for assessing the performance of the time domain, frequency domain and fusion of time-frequency domain features, with a five-fold cross-validation. Four different performance measures (sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve) were used to measure the overall performance, and the results suggested that the classifiers based on time-frequency domain features perform the best while using combinedly. Using the utilized fusion of the time-frequency domain features, the classification models can identify the smoker group with an accuracy ranged from (86.5-91.3%), where the RFC shows the best accuracy of 91.3%, which is higher than the three other classifiers models.
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ID Code: | 211135 | ||||
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Item Type: | Contribution to Journal (Journal Article) | ||||
Refereed: | Yes | ||||
ORCID iD: |
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Measurements or Duration: | 8 pages | ||||
DOI: | 10.25046/aj060193 | ||||
ISSN: | 2415-6698 | ||||
Pure ID: | 86306307 | ||||
Divisions: | Current > QUT Faculties and Divisions > Faculty of Health Current > Schools > School of Psychology & Counselling |
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Copyright Owner: | Consult author(s) regarding copyright matters | ||||
Copyright Statement: | All articles published by ASTES Journal are made immediately available worldwide under an open access license. ASTES Journal started to publish articles under the Creative Commons Attribution License and are now using the latest version of the CC BY license, which grants authors the most extensive rights. | ||||
Deposited On: | 15 Jun 2021 23:45 | ||||
Last Modified: | 20 Apr 2024 19:52 |
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