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Fusion: Practice and Applications
Volume 2 , Issue 1, PP: 14-21 , 2020 | Cite this article as | XML | Html |PDF


Detection and Classification of Alcoholics Using Electroencephalogram Signal and Support Vector Machine

Authors Names :   Shaymaa Adnan Abdulrahman   1 *     Rafah Amer Jaafar   2  

1  Affiliation :  Department of computer Engineering, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq

    Email :  Shaymaaa416@gmail.com

2  Affiliation :  Department of computer Engineering, Imam Ja’afar Al-Sadiq University, Baghdad, Iraq

    Email :  rafah_amer@ yahoo.com

Doi   :   https://doi.org/10.54216/FPA.020103

Received: March 15, 2020 Revised: May 10, 2020 Accepted: June 29, 2020

Abstract :

Alcoholism may be recognized with the use of (EEG) analyzing signals. None-the-less, the analysis of the multi-channel signals of EEG is a complicated issue that usually needs performing complex computation operations and takes quite a long time to execute. The presented research will propose 13 optimal channel to feature extraction. In this research, an innovative horizontal visibility graph entropy (HVGE) method has been proposed for evaluating signals of EEG from controlled drinkers and alcoholic subjects and comparing against an approach of sample entropy (SaE). Values of HVGE and SaE have been obtained from 1200 records of bio-medical signals.  While  in classification step using SVM as classifier.

Keywords :

Alcoholics ,   Support Vector Machine , Using  Electroencephalogram Signal , Sample Entropy , Classification.

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Cite this Article as :
Shaymaa Adnan Abdulrahman , Rafah Amer Jaafar, Detection and Classification of Alcoholics Using Electroencephalogram Signal and Support Vector Machine, Fusion: Practice and Applications, Vol. 2 , No. 1 , (2020) : 14-21 (Doi   :  https://doi.org/10.54216/FPA.020103)