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Dual-Tree Complex Wavelet Transform-Based Features for Automated Alcoholism Identification

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Abstract

A novel automated system for the identification of alcoholic subjects using electroencephalography (EEG) signals is proposed in this study. The proposed system employed dual-tree complex wavelet transform (DTCWT)-based features and sequential minimal optimization support vector machine (SMO-SVM), least square support vector machine (LS-SVM), and fuzzy Sugeno classifiers (FSC) for the automated identification of alcoholic EEG signals. The EEG signals are decomposed into several sub-bands (SBs) using DTCWT. The features extracted from DTCWT-based SBs are fed to FSC, SMO-SVM, and LS-SVM classifiers to evaluate the best performing classifier. The tenfold cross-validation scheme is used to mitigate the overfitting of the model. We have obtained the highest classification accuracy (CAC) of 97.91%, the area under receiver operating characteristic curve (AU-ROC) of 0.999 and Matthews correlation coefficient (MCC) of 0.958 for our proposed alcoholic diagnosis model. Our alcoholism detection system performed better than the existing systems in terms of all three measures: CAC, AU-ROC, and MCC.

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Authors and Affiliations

  1. Department of Electrical Engineering, Institute of Infrastructure, Technology, Research and Management (IITRAM), Ahmedabad, 380026, India

    Manish Sharma

  2. Department of Electronics and Communication Engineering, Acropolis Institute of Technology and Science, Indore, India

    Pragya Sharma

  3. Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore, India

    Ram Bilas Pachori

  4. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore, 599489, Singapore

    U. Rajendra Acharya

  5. Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore

    U. Rajendra Acharya

  6. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    U. Rajendra Acharya

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  1. Manish Sharma
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  2. Pragya Sharma
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  4. U. Rajendra Acharya
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Sharma, M., Sharma, P., Pachori, R.B. et al. Dual-Tree Complex Wavelet Transform-Based Features for Automated Alcoholism Identification. Int. J. Fuzzy Syst. 20, 1297–1308 (2018). https://doi.org/10.1007/s40815-018-0455-x

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