Skip to main content
SpringerLink
Log in

Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

In this study, the magnitude and spatial distribution of frequency spectrum in the resting electroencephalogram (EEG) were examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic conditions (n = 20) and the control group (n = 20). Data were taken from motor cortex region and divided into five sub-bands (delta, theta, alpha, beta-1 and beta-2). Three methodologies were adopted for feature extraction: (1) absolute power, (2) relative power and (3) peak power frequency. The dimension of the extracted features is reduced by linear discrimination analysis and classified by support vector machine (SVM) and fuzzy C-mean clustering. The maximum classification accuracy (88 %) with SVM clustering was achieved with the EEG spectral features with absolute power frequency on F4 channel. Among the bands, relatively higher classification accuracy was found over theta band and beta-2 band in most of the channels when computed with the EEG features of relative power. Electrodes wise CZ, C3 and P4 were having more alteration. Considering the good classification accuracy obtained by SVM with relative band power features in most of the EEG channels of motor cortex, it can be suggested that the noninvasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Anton RF (1999) What is craving? Models and implications for treatment. Alcohol Res Health 23:165–173

    CAS  PubMed  Google Scholar 

  2. Benerji DSN, Rajini K, Rao BS, Banerjee DRN, Rani KS, Rajkumar G, Ayyanna C (2010) Studies on physico-chemical and nutritional parameters for the production of ethanol from Mahua flower (Madhucaindica) using Saccharomyces Cerevisiae—3090 through submerged fermentation (smf). J Microb Biochem Technol 2:46–50

    Article  CAS  Google Scholar 

  3. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York

    Book  Google Scholar 

  4. Campanell S, Petit G, Maurage P, Kornreich C, Verbanck P, Noël X (2009) Chronic alcoholism: insights from neurophysiology. Clin Neurophys 39:191–207

    Article  Google Scholar 

  5. Cohen HL, Porjesz B, Begleiter H (1991) EEG characteristics in males at risk for alcoholism. Alcohol Clin Exp Res 15:858–861

    Article  CAS  PubMed  Google Scholar 

  6. Daskalakis ZJ, Christensen BK, Fitzgerald PB, Roshan L, Chen R (2002) The mechanisms of interhemispheric inhibition in the human motor cortex. J Physiol 543:317–326

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Deng C, He X, Han J (2008) SRDA: an efficient algorithm for large scale discriminant analysis. IEEE Trans Knowl Data Eng 20:1–12

    Article  Google Scholar 

  8. Fein G, Sclafani DV, Cardenas VA, Goldmann H, Tolou-Shams M, Meyerhoff DJ (2002) Cortical gray matter loss in treatment-naive alcohol dependent individuals. Alcohol Clin Exp Res 26:558–564

    PubMed Central  CAS  PubMed  Google Scholar 

  9. George MS, Anton RF, Bloomer C, Tenback C, Drobes DJ, Lorberbaum JP, Nahas Z, Vincent DJ (2001) Activation of prefrontal cortex and anterior thalamus in alcoholic subjects on exposure to alcohol-specific case. Arch Gen Psychiatry 58:345–352

    Article  CAS  PubMed  Google Scholar 

  10. Gilbertson R, Prather R, Nixon SJ (2008) The role of selected factors in the development and consequences of alcohol dependence. Alcohol Res Health 31:389–399

    PubMed Central  PubMed  Google Scholar 

  11. Grant AK, Stafford J, Thiede A, Kiley C, Odagiri M, Ferguson B (2008) Who is at risk? Population characterization of alcohol self administration in nonhuman primates helps identify pathways to dependence. Alcohol Res Health 31:289–297

    PubMed Central  PubMed  Google Scholar 

  12. Guénel P, Cyr D, Sabroe S, Lynge E, Merletti F, Ahrens W, Baumgardt-Elms C, Ménégoz F, Olsson H, Paulsen S, Simonato L, Wingren G (2004) Alcohol drinking may increase risk of breast cancer in men: a European population-based case–control study. Cancer Causes Control 15:571–580

    Article  PubMed  Google Scholar 

  13. Hanchar HJ, Dodson PD, Olsen RW, Otis TS, Wallner M (2005) Alcohol-induced motor impairment caused by increased extrasynaptic GABAA receptor activity. Nat Neurosci 8:339–345

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  14. Holroyd CB, Yeung N (2003) Alcohol and error processing. Trends Neurosci 26:402–404

    Article  CAS  PubMed  Google Scholar 

  15. Hommer DW (2003) Male and female sensitivity to alcohol-induced brain damage. Alcohol Res Health 27:181–185

    PubMed  Google Scholar 

  16. Kähkönen S, Kesäniemi M, Nikouline VV, Karhu J, Ollikainen M, Holi M, Ilmoniemi RJ (2001) Ethanol modulates cortical activity: direct evidence with combined TMS and EEG. Neuroimage 14:322–328

    Article  PubMed  Google Scholar 

  17. Kähkönen S, Wilenius J, Nikulin VV, Ollikainen M, Ilmoniemi RJ (2003) Alcohol reduces prefrontal cortical excitability in humans: a combined TMS and EEG study. Neuropsychopharmacology 28:747–754

    Article  PubMed  Google Scholar 

  18. Kim DJ, Jeong J, Kim KS, Chae JH, Jin SH, Ahn KJ, Myrick H, Yoon SJ, Kim HR, Kim SY (2003) Complexity changes of the EEG induced by alcohol cue exposure in alcoholics and social drinkers. Alcohol Clin Exp Res 27:1955–1961

    Article  PubMed  Google Scholar 

  19. Lintunen M, Hyytiä P, Sallmen T, Karlstedt K, Tuomisto L, Leurs R, Kiianmaa K, Korpi ER, Panula P (2001) Increased brain histamine in an alcohol-preferring rat line and modulation of ethanol consumption by H3 receptor mechanisms. FASEB J 15:1074–1076

    CAS  PubMed  Google Scholar 

  20. Lovinger DM (2008) Communication networks in the brain: neurons, receptors, neurotransmitters, and alcohol. Alcohol Res Health 31:196–214

    PubMed Central  PubMed  Google Scholar 

  21. Mann RE, Smart RG, Govoni R (2003) The epidemiology of alcoholic liver disease. Alcohol Res Health 27:209–219

    PubMed  Google Scholar 

  22. Nardone R, Bergmann J, Kronbichler M, Caleri F, Lochner P, Tezzon F, Ladurner G, Golaszewski S (2010) Altered motor cortex excitability to magnetic stimulation in alcohol withdrawal syndrom. Alcohol Clin Exp Res 34:628–632

    Article  PubMed  Google Scholar 

  23. Nute CH, Marasa J, Itil TM (1971) Spectral analysis of the EEG on the PDP-1. In: Wulfsohn NL, Sances A Jr (eds) The nervous system and electric currents, vol 2. Springer, Berlin, pp 57–58

  24. Oscar-Berman M, Marinkovic K (2003) Alcoholism and the brain: an overview. Alcohol Res Health 27:125–133

    PubMed  Google Scholar 

  25. Oscar-Berman M, Marinkovic K (2007) Alcohol: effects on neurobehavioral functions and the brain. Neuropsychol Rev 17:239–257

    Article  PubMed Central  PubMed  Google Scholar 

  26. Padmanabhapillai A, Tang Y, Ranganathan M, Rangaswamy M, Jones KA, Chorlian DB, Kamarajan C, Stimus A, Kuperman S, Rohrbaugh J, O’Connor SJ, Bauer LO, Schuckit MA, Begleiter H, Porjesz B (2006) Evoked gamma band response in male adolescent subjects at high risk for alcoholism during a visual oddball task. Int J Psychophysiol 62:262–271

    Article  PubMed  Google Scholar 

  27. Patkar AA, Sterling RC, Gottheil E, Weinstein SP (1999) A comparison of medical symptoms reported by cocaine-, opiate-, and alcohol-dependent patients. Subst Abuse 20:227–235

    Google Scholar 

  28. Popova EN (1997) Changes in neurons of the caudate nucleus in experimental alcoholism. Biull Eksp Biol Med 124:66–69

    Article  CAS  PubMed  Google Scholar 

  29. Porjesz B, Begleiter H (2003) Alcoholism and human electrophysiology. Alcohol Res Health 27:153–160

    PubMed  Google Scholar 

  30. Porjesz B, Rangaswamy M, Kamarajan C, Jones KA, Padmanabhapillai A, Begleiter H (2005) The utility of neurophysiological markers in the study of alcoholism. Clin Neurophysiol 116:993–1018

    Article  PubMed  Google Scholar 

  31. Rangaswamy M, Porjesz B, Chorlian DB, Wang K, Jones KA, Bauer LO, Rohrbaugh J, O’Connor SJ, Kuperman S, Reich T, Begleiter H (2002) Beta power in the EEG of alcoholics. Biol Psychiatry 51:831–842

    Article  Google Scholar 

  32. Rangaswamy M, Porjesz B, Chorlian DB, Wang K, Jones KA, Kuperman S, Rohrbaugh J, O’Connor SJ, Bauer LO, Reich T, Begleiter H (2004) Resting EEG in offspring of male alcoholics: beta frequencies. Int J Psychophysiol 51:239–251

    Article  PubMed  Google Scholar 

  33. Slawecki CJ, Thorsell A, Ehlers CL (2004) Lon-term effect of alcohol or nicotine exposure in adolescent animal models. Ann NY Acad Sci 1021:448–452

    Article  CAS  PubMed  Google Scholar 

  34. Wong DF, Maini A, Rousset OG, Brašić JR (2003) Positron emission tomography: a tool for identifying the effects of alcohol dependence on the brain. Alcohol Res Health 27:161–173

    PubMed  Google Scholar 

  35. Xie XL, Beni GA (1991) Validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 3:841–861

    Article  Google Scholar 

Download references

Acknowledgments

Authors acknowledge the help received from Mr. Ramesh Kumar, M.E. Student, Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi (India) for the analysis of EEG data.

Author information

Authors and Affiliations

  1. Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, Jharkhand, India

    Surendra Kumar & Rakesh Kumar Sinha

  2. Department of Electrical Engineering, National Institute of Technology, Raipur, 492010, Chhatisgarh, India

    Subhojit Ghosh

  3. Department of Physiology, Rajendra Institute of Medical Sciences, Ranchi, 834009, Jharkhand, India

    Suhash Tetarway

Authors
  1. Surendra Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Subhojit Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Suhash Tetarway
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rakesh Kumar Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rakesh Kumar Sinha.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, S., Ghosh, S., Tetarway, S. et al. Support vector machine and fuzzy C-mean clustering-based comparative evaluation of changes in motor cortex electroencephalogram under chronic alcoholism. Med Biol Eng Comput 53, 609–622 (2015). https://doi.org/10.1007/s11517-015-1264-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-015-1264-0

Keywords

Search

Navigation