Abstract
Identification of alcoholism is an important task because it affects the operation of the brain. Alcohol consumption, particularly heavier drinking is identified as an essential factor to develop health issues, such as high blood pressure, immune disorders, and heart diseases. To support health professionals in diagnosis disorders related with alcoholism with a high rate of accuracy, there is an urgent demand to develop an automated expert systems for identification of alcoholism. In this study, an expert system is proposed to identify alcoholism from multi-channel EEG signals. EEG signals are partitioned into small epochs, with each epoch is further divided into sub-segments. A covariance matrix method with its eigenvalues is utilised to extract representative features from each sub-segment. To select most relevant features, a statistic approach named Kolmogorov–Smirnov test is adopted to select the final features set. Finally, in the classification part, a robust algorithm called AdaBoost k-means (AB-k-means) is designed to classify EEG features into two categories alcoholic and non-alcoholic EEG segments. The results in this study show that the proposed model is more efficient than the previous models, and it yielded a high classification rate of 99%. In comparison with well-known classification algorithms such as K-nearest k-means and SVM on the same databases, our proposed model showed a promising result compared with the others. Our findings showed that the proposed model has a potential to implement in automated alcoholism detection systems to be used by experts to provide an accurate and reliable decisions related to alcoholism.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pelvig, D.P., Pakkenberg, H., Stark, A.K., Pakkenberg, B.: Neocortical glial cell numbers in human brains. Neurobiol. Aging 29(11), 1754–1762 (2008)
Deiner, S., Silverstein, J.: Postoperative delirium and cognitive dysfunction. Br. J. Anaesth. 103(suppl_1), i41–i46 (2009)
Volkow, N.D., Wiers, C.E., Shokri-Kojori, E., Tomasi, D., Wang, G.-J., Baler, R.: Neurochemical and metabolic effects of acute and chronic alcohol in the human brain: studies with positron emission tomography. Neuropharmacology 122, 175–188 (2017)
Lieber, C.S.: Medical disorders of alcoholism. N. Engl. J. Med. 333(16), 1058–1065 (1995)
Oscar-Berman, M., Shagrin, B., Evert, D.L., Epstein, C.: Impairments of brain and behavior: the neurological effects of alcohol. Alcohol Health Res. World 21(1), 65 (1997)
Acharya, U.R., Bhat, S., Adeli, H., Adeli, A.: Computer-aided diagnosis of alcoholism-related EEG signals. Epilepsy Behav. 41, 257–263 (2014)
Faust, O., Acharya, R., Allen, A.R., Lin, C.: Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques. IRBM 29(1), 44–52 (2008)
Patidar, S., Pachori, R.B., Upadhyay, A., Acharya, U.R.: An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism. Appl. Soft Comput. 50, 71–78 (2017)
Shooshtari, M.A., Setarehdan, S.K.: Selection of optimal EEG channels for classification of signals correlated with alcohol abusers. In: IEEE 10th International Conference on Signals Processing, pp. 1–4. IEEE, Beijing (2010)
Kumar, Y., Dewal, M., Anand, R.: Features extraction of EEG signals using approximate and sample entropy. In: The 2012 IEEE Students Conference on Electrical, Electronics and Computer Science, pp. 1–5, IEEE, Bhopal (2012)
Cao, R., Deng, H., Wu, Z., Liu, G., Guo, H., Xiang, J.: Decreased synchronization in alcoholics using EEG. IRBM 38(2), 63–70 (2017)
Lin, C.-F., Yeh, S.-W., Chien, Y.-Y., Peng, T.-I., Wang, J.-H., Chang, S.-H.: A HHT-based time frequency analysis scheme for clinical alcoholic EEG signals. In: The WSEAS International Conference. Proceedings of the Mathematics and Computers in Science and Engineering, no. 9. World Scientific and Engineering Academy and Society (2009)
Kousarrizi, M.R.N., Ghanbari, A.A., Gharaviri, A., Teshnehlab, M., Aliyari, M.: Classification of alcoholics and non-alcoholics via EEG using SVM and neural networks. In: 3rd International Conference on Bioinformatics and Biomedical Engineering, pp. 1–4, Beijing. IEEE (2009)
Sadiq, M.T., Yu, X., Yuan, Z., Aziz, M.Z., Siuly, S., Ding, W.: A matrix determinant feature extraction approach for decoding motor and mental imagery EEG in subject specific tasks. IEEE Trans. Cogn. Dev. Syst. (2020). https://doi.org/10.1109/TCDS.2020.3040438
Sadiq, M.T., Yu, X., Yuan, Z.: Exploiting dimensionality reduction and neural network techniques for the development of expert brain–computer interfaces. Expert Syst. Appl. 164, 114031 (2020)
Hettich, S., Bay, S.: The UCI KDD Archive. University of California, Irvine, CA. Department of Information and Computer Science 152 (1999). http://kdd.ics.uci.edu
Zhang, X.L., Begleiter, H., Porjesz, B., Litke, A.: Electrophysiological evidence of memory impairment in alcoholic patients. Biol. Psychiat. 42(12), 1157–1171 (1997)
Diykh, M., Li, Y., Wen, P.: Classify epileptic EEG signals using weighted complex networks-based community structure detection. Expert Syst. Appl. 90, 87–100 (2017)
Diykh, M., Li, Y., Wen, P., Li, T.: Complex networks approach for depth of anesthesia assessment. Measurement 119, 178–189 (2018)
Diykh, M., Miften, F.S., Abdulla, S., Saleh, K., Green, J.H.: Robust approach to depth of anaesthesia assessment based on hybrid transform and statistical features. IET Sci. Meas. Technol. 14(1), 128–136 (2019)
Miften, F.S., Diykh, M., Abdulla, S., Siuly, S., Green, J.H., Deo, R.C.: A new framework for classification of multi-category hand grasps using EMG signals. Artif. Intell. Med. 112, 102005 (2021)
Faust, O., Yu, W., Kadri, N.A.: Computer-based identification of normal and alcoholic EEG signals using wavelet packets and energy measures. J. Mech. Med. Biol. 13(03), 1350033 (2013)
Faust, O., Yanti, R., Yu, W.: Automated detection of alcohol related changes in electroencephalograph signals. J. Med. Imaging Health Inform. 3(2), 333–339 (2013)
Kannathal, N., Acharya, U.R., Lim, C.M., Sadasivan, P.: Characterization of EEG—a comparative study. Comput. Methods Programs Biomed. 80(1), 17–23 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Diykh, M., Abdulla, S., Oudah, A.Y., Marhoon, H.A., Siuly, S. (2021). A Novel Alcoholic EEG Signals Classification Approach Based on AdaBoost k-means Coupled with Statistical Model. In: Siuly, S., Wang, H., Chen, L., Guo, Y., Xing, C. (eds) Health Information Science. HIS 2021. Lecture Notes in Computer Science(), vol 13079. Springer, Cham. https://doi.org/10.1007/978-3-030-90885-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-90885-0_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90884-3
Online ISBN: 978-3-030-90885-0
eBook Packages: Computer ScienceComputer Science (R0)