Optimal EEG channels selection for alcoholism screening using EMD domain statistical features and harmony search algorithm

Alcoholism can be analyzed by Electroencephalogram (EEG) data. Finding an optimal subset of EEG channels for alcoholism detection is a challenging task. The paper reports a new methodology for the detection of optimal channels for alcoholism analysis using EEG data. The proposed technique employs th...

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Bibliographic Details
Published in:Biocybernetics and biomedical engineering Vol. 41; no. 1; pp. 83 - 96
Main Authors: Bavkar, Sandeep, Iyer, Brijesh, Deosarkar, Shankar
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2021
Subjects:
Advanced Basic Science
Alcoholic
EEG
EMD
Ensemble subspace K NN
Harmony Search
Internal Medicine
Ensemble subspace K NN
Harmony Search
EMD
EEG
Alcoholic
ISSN:0208-5216
Online Access:Get full text
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Summary:Alcoholism can be analyzed by Electroencephalogram (EEG) data. Finding an optimal subset of EEG channels for alcoholism detection is a challenging task. The paper reports a new methodology for the detection of optimal channels for alcoholism analysis using EEG data. The proposed technique employs the Empirical Mode Decomposition (EMD) technique to extract the amplitude and frequency modulated bandwidth features from the Intrinsic Mode Function (IMF) and ensemble subspace K-NN as a classifier to classify alcoholics and normal. The optimum channels are selected, using a harmony search algorithm. The fitness value of discrete binary harmony search (DBHS) optimization algorithms is calculated using accuracy and sensitivity achieved by the ensemble subspace K-Nearest Neighbor classifier. Experimental outcomes indicate that the optimal channel selected by the harmony search algorithm has biological inference related to the alcoholic subject. The proposed approach reports a classification accuracy of 93.87%, with only 12 detected EEG channels.
ISSN:0208-5216
DOI:10.1016/j.bbe.2020.11.001