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An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals

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Abstract

This paper addresses an advanced analysis system for the identification of alcoholic brain states from electroencephalogram (EEG) data in an automatic way. This study introduces an optimum allocation based sampling (OAS) scheme to discover the most favourable representative data points from every single time-window of each EEG signal considering the minimal variability of the observations. Combining all representative samples of each time-window in a set, some statistical features are extracted from every set of each class. The Mann-Whitney U test is used to assess whether each of the features is significant between the two classes (e.g., alcoholic and control). In order to evaluate the effectiveness of the OAS-based features, four well-known machine learning methods (decision table, support vector machine (SVM), k-nearest neighbor (k-NN) and logistic regression) are considered for identification of alcoholic brain state. The experimental results on the UCI KDD (i.e., UCI knowledge discovery in databases) database demonstrate that the OAS based decision table algorithm yields the highest accuracy of 99.58% with a low false alarm rate 0.40%, which is an improvement of up to 9.58% over the existing algorithms. A proposed analysis system can be used to detect alcoholism and also to determine the level of alcoholism-related changes in EEG signals.

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References

  1. M. A. Enoch, D. Goldman. Problem drinking and alcoholism: Diagnosis and treatment. American Family Physician, vol. 65, no. 3, pp. 441–448, 2002.

    Google Scholar 

  2. World Health Organization (WHO). Global status report on alcohol and health, [Online], Available: https://apps.who.int/iris/bitstream/handle/10665/112736/9789240692763_eng.pdf;sequence=1, August 16, 2014.

    Google Scholar 

  3. S. S. Lim, T. Vos, A. D. Flaxman, G. Danaei, K. Shibuya. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysss for the Global Burden of Dieasee Study 0000. The Lancet, vol. 380, no. 9859, pp. 2224–2260, 2012. DOI: https://doi.org/10.1016/S0140-6736(12)61766-8.

    Article  Google Scholar 

  4. D. Endal. Global burden of disease figures show: Alcohol grows as risk factor for death and disability (corrected version), [Online], Available: http://www.add-resources.org/global-burden-of-diseasefigures-show-alcohol-grows-as-risk-factor-for-death-and-disability-correctedversion.5142425-315779.html, August 06, 2014.

    Google Scholar 

  5. MCDS (Mimsterial Council on Drug Strategy). The National Drug Strategy 2010–2015, Canberra, Australia: Commonwealth of Australia, 2011.

    Google Scholar 

  6. C. Harper. The neurotoxicity of alcohol. Human & Experimental Toxicology, vol. 26, no. 3, pp. 251–257, 2007. DOI: https://doi.org/10.1177/0960327107070499.

    Article  Google Scholar 

  7. J. C. M. Brust. Ethanol and cognition: indirect effects, neurotoxicity and neuroprotection: A review. International Journal of Environmental Research and Public Health, vol. 7, no. 4, pp. 1540–1557, 2010. DOI: https://doi.org/10.3390/ijerph7041540.

    Article  Google Scholar 

  8. N. A. Siuly, Y. Li, P. Wen. EEG signal classification based on simple random sampling technique with least square support vector machine. International Journal of Biomedical Engineering and Technology, vol. 7, no. 4, pp. 390–409, 2011. DOI: https://doi.org/10.1504/IJBET.2011.044417.

    Article  Google Scholar 

  9. U. R. Acharya, S. Vidya, S. Bhat, H. Adeli, A. Adeli. Computer-aided diagnosis of alcoholism-related EEG signals. Epilepsy & Behavior, vol. 41, pp. 257–263, 2014. DOI: https://doi.org/10.1016/j.yebeh.2014.10.001.

    Article  Google Scholar 

  10. C. L. Ehlers, J. W. Havstad. Characterization of drug effects on the EEG by power spectral band time series analysis. Psychopharmacology Bulletin, vol. 18, no. 3, pp. 43–47, 1982.

    Google Scholar 

  11. N. Kannathal, U. R. Acharya, C. M. Lim, P. K. Sadasivan. Characterization of EEG-A comparative study. Computer Methods and Programs in Biomedicine, vol. 80, no. 1, pp. 17–23, 2005. DOI: https://doi.org/10.1016/j.cmpb.2005.06.005.

    Article  Google Scholar 

  12. U. R. Acharya, S. V. Sree, S. Chattopadhyay, J. S. Suri. Automated diagnosis of normal and alcoholic EEG signals. International Journal of Neural Systems, vol. 22, no. 3, Article number 1250011, 2012. DOI: https://doi.org/10.1142/S0129065712500116.

    Google Scholar 

  13. O. Faust, R. U. Acharya, A. R. Allen, C. M. Lin. Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques. IRBM, vol. 29, no. 1, pp. 44–52, 2008. DOI: https://doi.org/10.1016/j.rbmret.2007.11.003.

    Article  Google Scholar 

  14. A. Yazdani, P. Ataee, S. K. Setarehdan, B. N. Araabi, C. Lucas. Neural, fuzzy and neurofuzzy approach to classification of normal and alcoholic electroencephalograms. In Proceedings of the 5th International Symposium on Image and Signal Processing and Analysis, IEEE, Istanbul, Turkey, 2007. DOI: https://doi.org/10.1109/ISPA.2007.4383672.

    Book  Google Scholar 

  15. Y. G. Sun, N. Ye, X. H. Xu. EEG analysis of alcoholics and controls based on feature extraction. In Proceedings of the 8th International Conference on Signal Processing, IEEE, Beijing, China, pp. 16–20, 2006. DOI: https://doi.org/10.1109/ICOSP.2006.344501.

    Google Scholar 

  16. P. Coutin-Churchman, R. Moreno, Y. Añez, F. Vergara. Clinical correlates of quantitative EEG alterations in alcoholic patients. Clinical Neurophysiology, vol. 117, no. 4, pp. 740–751, 2006. DOI: https://doi.org/10.1016/j.clinph.2005.12.021.

    Article  Google Scholar 

  17. T. K. Padma, N. Sriraam. EEG based detection of alcoholics using spectral entropy with neural network classifiers. In Proceedings of International Conference on Biomedical Engineering, IEEE, Penang, Malaysia, pp. 89–93, 2012. DOI: https://doi.org/10.1109/ICoBE.2012.6178961.

    Google Scholar 

  18. G. H. Zhu, Y. Li, P. Wen, S. F. Wang. Analysis of alcoholic EEG signals based on horizontal visibility graph entropy. Brain Informatics, vol. 1, no. 1–4, pp. 19–25, 2014. DOI: https://doi.org/10.1007/s40708-014-0003-x.

    Article  Google Scholar 

  19. EEG Database. UCI KDD archive, [Online], Available: http://kdd.ics.uci.edu/databases/eeg/eeg.data.html, October 13, 1999.

    Google Scholar 

  20. Siuly, Y. Li. A novel statistical algorithm for multiclass EEG signal classification. Engineering Applications of Artificial Intelligence, vol. 34, pp. 154–167, 2014. DOI: https://doi.org/10.1016/j.engappai.2014.05.011.

    Article  Google Scholar 

  21. M. N. Islam. An Introduction to Sampling Methods: Theory and Applications, Dhaka, Bengal: Book World, Dhaka New Market & P.K. Roy Road, 2007.

    Google Scholar 

  22. S. Siuly, X. X. Yin, S. Hadjiloucas, Y. C. Zhang. Classification of THz pulse signals using two-dimensional cross-correlation feature extraction and non-linear classifiers. Computer Methods and Programs in Biomedicine, vol. 127, pp. 64–82, 2016. DOI: https://doi.org/10.1016/j.cmpb.2016.01.017.

    Article  Google Scholar 

  23. R. Kohavi. The power of decision tables. In Proceedings of the 8th European Conference on Machine Learning, Springer, Heraclion, Greece, pp. 174–189, 1995. DOI: https://doi.org/10.1007/3-540-59286-5_57.

    Google Scholar 

  24. V. N. Vapnik. The Nature of Statistical Learning Theory, New York, USA: Springer, 2000. DOI: https://doi.org/10.0007/978-1-4757-2440-0.

    Book  Google Scholar 

  25. M. Goudjil, M. Koudil, M. Bedda, N. Ghoggali. A novel active learning method using SVM for text classffication. International Journal of Automation and Computing, vol. 15, no. 3, pp. 290–298, 2018. DOI: https://doi.org/10.1007/s11633-015-0912-z.

    Article  Google Scholar 

  26. R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification, 2nd ed., New York, USA: Wiley, 2001.

    MATH  Google Scholar 

  27. S. Afrakhteh, M. R. Mosavi, M. Khishe, A. Ayatollahi. Accurate classification of EEG signals using neural networks trained by hybrid population-physic-based algorithm. International Journal of Automation and Computing, 2018, DOI: https://doi.org/10.1007/s11633-018-1158-3. to be published.

    Article  Google Scholar 

  28. D. W. Jr. Hosmer, S. Lemeshow. Applied Logistic Regression, New York, USA: Wiley, 1989.

    MATH  Google Scholar 

  29. F. Hernandez, L. C. Wu, M. C. Yip, K. Laksari, A. R. Hoffman, J. R. Lopez, G. A. Grant, S. Kleiven, D. B. Camarillo. Six degree-of-freedom measurements of human mild traumatic brain injury. Annals of Biomedical Engineering, vol. 43, no. 8, pp. 1918–1934, 2015. DOI: https://doi.org/10.1007/s10439-014-1212-4.

    Article  Google Scholar 

  30. R. Zarei, J. He, S. Siuly, Y. C. Zhang. A PCA aided cross-covariance scheme for discriminative feature extraction from EEG signals. Computer Methods and Programs in Biomedicine, vol. 146, pp. 47–57, 2017. DOI: https://doi.org/10.1016/j.cmpb.2017.05.009.

    Article  Google Scholar 

  31. S. A. Imtiaz, E. Rodriguez-Villegas. A low computational cost algorithm for rem sleep detection using single channel EEG. Annals of Biomedical Engineering, vol. 42, no. 11, pp. 2344–2359, 2014. DOI: https://doi.org/10.1007/s10439-014-1085-6.

    Article  Google Scholar 

  32. V. Bajaj, R. B. Pachori. Automatic classification of sleep stages based on the time-frequency image of EEG signals. Computer Methods and Programs in Biomedicine, vol. 112, no. 3, pp. 320–328, 2013. DOI: https://doi.org/10.1016/j.cmpb.2013.07.006.

    Article  Google Scholar 

  33. S. Siuly, Y. Li. Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification. Computer Methods and Programs in Biomedicine, vol. 119, no. 1, pp. 29–12, 2015. DOI: https://doi.org/10.1016/j.cmpb.2015.01.002.

    Article  Google Scholar 

  34. A. R. Hassan, S. Siuly, Y. C. Zhang. Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Computer Methods and Programs in Biomedicine, vol. 137, pp. 247–259, 2016. DOI: https://doi.org/10.1016/j.cmpb.2016.09.008.

    Article  Google Scholar 

  35. O. Faust, W. W. Yu, N. A. Kadri. Computer-based identification of normal and alcoholic EEG signals using wavelet packets and energy measures. Journal of Mechanics in Medicine and Biology, vol. 13, no. 3, Article number 1350033, 2013. DOI: https://doi.org/10.1142/S0219519413500334.

    Google Scholar 

  36. V. Bajaj, Y. H. Guo, A. Sengur, S. Siuly, O. F. Alcin. A hybrid method based on time-frequency images for classification of alcohol and control EEG signals. Neural Computing and Applications, vol. 28, no. 12, pp. 3717–3723, 2017. DOI: https://doi.org/10.1007/s00521-016-2276-x.

    Article  Google Scholar 

  37. S. Patidar, R. B. Pachori, A. Upadhyay, U. R. Acharya. An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism. Applied Soft Computing, vol. 50, pp. 71–78, 2007. DOI: https://doi.org/10.1016/j.asoc.2016.11.002.

    Article  Google Scholar 

  38. C. L. Ehlers, J. Havstad, D. Prichard, J. Theiler. Low doses of ethanol reduce evidence for nonlinear structure in brain activity. Journal of Neuroscience, vol. 18, no. 18, pp. 7474–7486, 1998. DOI: https://doi.org/10.1523/JNEUROSCI.18-18-07474.1998.

    Article  Google Scholar 

  39. O. Faust, R. Yanti, W. W. Yu. Automated detection of alcohol related changes in electroencephalograph signals. Journal of Medical Imaging and Health Informatics, vol. 3, no. 2, pp. 333–339, 2013. DOI: https://doi.org/10.1166/jmihi.2013.1170.

    Article  Google Scholar 

  40. S. Taran, V. Bajaj. Rhythm-based identification of alcohol EEG signals. IET Science, Measurement & Technology, vol. 12, no. 3, pp. 343–349, 2018. DOI: https://doi.org/10.1049/iet-smt.2017.0232.

    Article  Google Scholar 

  41. M. Sharma, P. Sharma, R. B. Pachori, U. R. Acharya. Dual-tree complex wavelet transform-based features for automated alcoholism identification. International Journal of Fuzzy Systems, vol. 20, no. 4, pp. 1297–1308, 2018. DOI: https://doi.org/10.1007/s40815-018-0455-x.

    Article  Google Scholar 

  42. A. Priya, P. Yadav, S. Jain, V. Bajaj. Efficient method for classification of alcoholic and normal EEG signals using EMD. The Journal of Engineering, vol. 2018, no. 3, pp. 166–172, 2018. DOI: https://doi.org/10.1049/joe.2017.0878.

    Article  Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61332013) and the Australian Research Council (ARC) Linkage Project (No. LP100200682) and Discovery Project (No. DP140100841)

Author information

Authors and Affiliations

  1. Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, VIC, 3011, Australia

    Siuly Siuly & Yanchun Zhang

  2. Discipline of Electronics and Communication Engineering, PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India

    Varun Bajaj

  3. Deptartement of Electrical and Electronics Engineering, Faculty of Technology, Firat University, Elazig, 23119, Turkey

    Abdulkadir Sengur

  4. Cyberspace Institute of Advanced Technology (CIAT), Guangzhou University, Guangzhou, 510006, China

    Yanchun Zhang

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  1. Siuly Siuly
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Correspondence to Siuly Siuly.

Additional information

Recommended by Associate Editor Hong Qiao

Siuly Siuly received the Ph. D. degree in biomedical engineering from the University of Southern Queensland, Australia in 2012. She is currently a research fellow with the Institute for Sustainable Industries and Liveable Cities, College of Engineering and Science, Victoria University, Australia. She already developed some breakthrough methods in the mentioned areas. She made significant contributions to the stated research fields publishing top quality journals/conferences including IEEE Transactions on Neural Systems and Rehabilitation Engineering, Engineering Applications of Artificial Intelligence, IEEE Transactions on Emerging Topics in Computational Intelligence, IEEE Access, Computer Methods and Programs in Biomedicine, Neurocomputing, etc.

Her research interests include biomedical signal processing, analysis and classification, detection and prediction of neurological abnormality from brain signal data, brain-computer interface, machine learning, pattern recognition, artificial intelligence, and medical data mining.

Varun Bajaj received B. Eng. degree in electronics and communication engineering from Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV), India in 2006, the M. Tech. (Hons.) degree in microelectronics and VLSI (very large scale integration) design from Shri Govindram Seksaria Institute of Technology and Science (SGSITS), India in 2009, the Ph. D. degree in electrical engineering from Indian Institute of Technology (IIT), India in 2014. Presently, he is working as assistant professor with the Discipline of Electronics and Communication Engineering, at Indian Institute of Information Technology, Design and Manufacturing, India. He has authored more than 80 research papers in various reputed international journals/conferences like IEEE Transactions, Elsevier, Springer, Institute of Physics (IOP), etc. He is a recipient of various reputed national and international awards. He is also serving as a subject editor of Institution of Engineering and Technology (IET) Electronics letters and active technical reviewer of leading international journals like IEEE, IET, and Elsevier, etc.

His research interests include biomedical signal processing, image processing and time-frequency analysis, speech processing.

Abdulkadir Sengur received the B. Sc. degree in electronics and computers education from the Firat University, Turkey in 1999, and the M. Sc. degree in electronics education from the Firat University, Turkey in 2003, and the Ph. D. degree in electrical and electronics engineering from the Firat University, Turkey in 2006. He became a research assistant in the Technical Education Faculty of Firat University in February 2001. He is currently a professor in the Technology Faculty of Firat University, Turkey.

His research interests include signal processing, image segmentation, pattern recognition, medical image processing and computer vision.

Yanchun Zhang received the Ph. D. degree in computer science from University of Queensland, Australia in 1991. He is currently the director of Information technology Program (Data Science and Artificial Intelligence) with the Institute for Sustainable Industries & Liveable Cities, Victoria University (VU) Research, Victoria University, Australia and coordinates a multidisciplinary e-research program across Victoria University. He is also an international research leader in databases, data mining, health informatics, Web information systems, and Web services. He has authored over 220 research papers in international journals and conference proceedings, and authored/edited 12 books. He is the Editor-in-Chief of World Wide Web Journal (Springer) and the Health Information Science and Systems (Bio-Med Central). He is also the Chairman of the International Web Information Systems Engineering Society.

His research interests include data mining, pattern recognition, machine learning, biomedical signal processing, databases, data management, e-health, environmental studies and sensor networks.

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Siuly, S., Bajaj, V., Sengur, A. et al. An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals. Int. J. Autom. Comput. 16, 737–747 (2019). https://doi.org/10.1007/s11633-019-1178-7

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