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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| Published in: | Biocybernetics and biomedical engineering Vol. 41; no. 1; pp. 83 - 96 |
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| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.01.2021
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| ISSN: | 0208-5216 |
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| Abstract | 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. |
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| AbstractList | 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. AbstractAlcoholism 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. |
| Author | Iyer, Brijesh Bavkar, Sandeep Deosarkar, Shankar |
| Author_xml | – sequence: 1 givenname: Sandeep orcidid: 0000-0001-9692-853X surname: Bavkar fullname: Bavkar, Sandeep email: bavkar_ss@rediffmail.com – sequence: 2 givenname: Brijesh orcidid: 0000-0003-0152-3527 surname: Iyer fullname: Iyer, Brijesh email: brijeshiyer@dbatu.ac.in – sequence: 3 givenname: Shankar surname: Deosarkar fullname: Deosarkar, Shankar email: sbdeosarkar@yahoo.com |
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| Cites_doi | 10.1049/joe.2017.0878 10.1111/j.1530-0277.2009.01136.x 10.1016/j.patrec.2019.04.019 10.1016/j.clinph.2011.06.001 10.1016/j.neucom.2013.05.005 10.1177/003754970107600201 10.1007/s40708-014-0003-x 10.1007/s11571-017-9465-x 10.1113/jphysiol.2002.017673 10.1088/1361-6579/aa6b4c 10.1142/S0129065712500116 10.2174/15672026113109990004 10.1007/s40815-018-0455-x 10.1049/iet-smt.2017.0232 10.1038/s41598-017-01419-7 10.1007/s11633-019-1178-7 10.1111/adb.12481 10.1016/j.knosys.2016.04.026 10.1016/S0006-3223(96)00552-5 10.1098/rspa.1998.0193 10.1007/s11571-016-9416-y 10.20965/jaciii.2011.p1221 10.1007/s40708-017-0061-y 10.1038/s41598-017-18471-y 10.1109/ACCESS.2019.2927267 10.1109/72.991435 10.1016/j.artmed.2017.11.002 |
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| Snippet | Alcoholism can be analyzed by Electroencephalogram (EEG) data. Finding an optimal subset of EEG channels for alcoholism detection is a challenging task. The... AbstractAlcoholism can be analyzed by Electroencephalogram (EEG) data. Finding an optimal subset of EEG channels for alcoholism detection is a challenging... |
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| SubjectTerms | Advanced Basic Science Alcoholic EEG EMD Ensemble subspace K NN Harmony Search Internal Medicine |
| Title | Optimal EEG channels selection for alcoholism screening using EMD domain statistical features and harmony search algorithm |
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