Advertisement

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

  • Siuly SiulyEmail author
  • Varun Bajaj
  • Abdulkadir Sengur
  • Yanchun Zhang
Research Article

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.

Keywords

Electroencephalogram (EEG) alcoholism optimum allocation technique feature extraction decision table 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

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)

References

  1. [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. [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. [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.CrossRefGoogle Scholar
  4. [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. [5]
    MCDS (Mimsterial Council on Drug Strategy). The National Drug Strategy 2010–2015, Canberra, Australia: Commonwealth of Australia, 2011.Google Scholar
  6. [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.CrossRefGoogle Scholar
  7. [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.CrossRefGoogle Scholar
  8. [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.CrossRefGoogle Scholar
  9. [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.CrossRefGoogle Scholar
  10. [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. [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.CrossRefGoogle Scholar
  12. [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.
  13. [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.CrossRefGoogle Scholar
  14. [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.CrossRefGoogle Scholar
  15. [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. [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.CrossRefGoogle Scholar
  17. [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. [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.CrossRefGoogle Scholar
  19. [19]
    EEG Database. UCI KDD archive, [Online], Available: http://kdd.ics.uci.edu/databases/eeg/eeg.data.html, October 13, 1999.Google Scholar
  20. [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.CrossRefGoogle Scholar
  21. [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. [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.CrossRefGoogle Scholar
  23. [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. [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.CrossRefzbMATHGoogle Scholar
  25. [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.CrossRefGoogle Scholar
  26. [26]
    R. O. Duda, P. E. Hart, D. G. Stork. Pattern Classification, 2nd ed., New York, USA: Wiley, 2001.zbMATHGoogle Scholar
  27. [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.
  28. [28]
    D. W. Jr. Hosmer, S. Lemeshow. Applied Logistic Regression, New York, USA: Wiley, 1989.zbMATHGoogle Scholar
  29. [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.CrossRefGoogle Scholar
  30. [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.CrossRefGoogle Scholar
  31. [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.CrossRefGoogle Scholar
  32. [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.CrossRefGoogle Scholar
  33. [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.CrossRefGoogle Scholar
  34. [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.CrossRefGoogle Scholar
  35. [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.
  36. [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.CrossRefGoogle Scholar
  37. [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.CrossRefGoogle Scholar
  38. [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.CrossRefGoogle Scholar
  39. [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.CrossRefGoogle Scholar
  40. [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.CrossRefGoogle Scholar
  41. [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.CrossRefGoogle Scholar
  42. [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.CrossRefGoogle Scholar

Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Gmbh Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute for Sustainable Industries & Liveable CitiesVictoria UniversityMelbourneAustralia
  2. 2.Discipline of Electronics and Communication EngineeringPDPM Indian Institute of Information Technology, Design and ManufacturingJabalpurIndia
  3. 3.Deptartement of Electrical and Electronics Engineering, Faculty of TechnologyFirat UniversityElazigTurkey
  4. 4.Cyberspace Institute of Advanced Technology (CIAT)Guangzhou UniversityGuangzhouChina

Personalised recommendations