Advertisement

Non Linear Autoregressive Model for Detecting Chronic Alcoholism

  • Surendra Kumar
  • Subhojit Ghosh
  • Suhash Tetarway
  • Shashank Sawai
  • Pillutla Soma Sunder
  • Rakesh Kumar Sinha
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)

Abstract

In this study, the Non Linear Autoregressive model of the resting electroencephalogram (EEG) was examined to address the problem of detecting alcoholism in the cerebral motor cortex. The EEG signals were recorded from chronic alcoholic (n = 20) conditions and the control group (n = 20). Data were taken from motor cortex region. The dimension of the extracted features are reduced by linear discrimination analysis (LDA) and classified by Support Vector Machine (SVM). The 600 sample from each group gave the best result using Support Vector Machine classifier. The maximum classification accuracy (90 %) with SVM clustering was achieved with the EEG Fz channel. More alterations are identified in the left hemisphere. Considering the good classification accuracy with SVM on Fz electrode, it can be suggested that the non-invasive automated online diagnostic system for the chronic alcoholic condition can be developed with the help of EEG signals.

Keywords

Alcohol Cerebral motor cortex Electroencephalogram LDA Non linear autoregressive model Support vector machine 

References

  1. 1.
    Rehm, J., Mathers, C., Popova, S., Thavorncharoensap, M., Teerawattananon, Y., Patra, J.: Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. Lancet 373(9682), 2223–2233 (2009)CrossRefGoogle Scholar
  2. 2.
    Patkar, A.A., Sterling, R.C., Gottheil, E., Weinstein, S.P.: A comparison of medical symptoms reported by cocaine-, opiate-, and alcohol-dependent patients. Subst. Abuse. 20, 227–235 (1999)Google Scholar
  3. 3.
    Daskalakis, Z.J., Christensen, B.K., Fitzgerald, P.B., Roshan, L., Chen, R.: The mechanisms of interhemispheric inhibition in the human motor cortex. J. Physiol. 543, 317–326 (2002)CrossRefGoogle Scholar
  4. 4.
    Cohen, H.L., Porjesz, B., Begleiter, H.: EEG characteristics in males at risk for alcoholism. Alcohol. Clin. Exp. Res. 15, 858–861 (1991)CrossRefGoogle Scholar
  5. 5.
    Benerji, D.S.N., Rajini, K., Rao, B.S., Banerjee, D.R.N., Rani, K.S., Rajkumar, G., Ayyanna, C.: Studies on physico-chemical and nutritional parameters for the production of ethanol from Mahua flower (Madhuca indica) using Saccharomyces Cerevisiae—3090 through submerged fermentation (SMF). J. Microb. Biochem. Tech. 2, 46–50 (2010)CrossRefGoogle Scholar
  6. 6.
    Ghosh, S., Maka, S.: Modeling based approach for evaluation of insulin sensitivity. Biomed. Signal Process. Control 4, 49–56 (2009)CrossRefGoogle Scholar
  7. 7.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)MATHGoogle Scholar
  8. 8.
    Porjesz, B., Rangaswamy, M., Kamarajan, C., Jones, K.A., Padmanabhapillai, A., Begleiter, H.: The utility of neurophysiological markers in the study of alcoholism. Clin. Neurophysiol. 116, 993–1018 (2005)CrossRefGoogle Scholar
  9. 9.
    Anton, R.F.: What is craving? Models and implications for treatment. Alcohol Res. Health. 23, 165–173 (1999)Google Scholar
  10. 10.
    George, M.S., Anton, R.F., Bloomer, C., Tenback, C., Drobes, D.J., Lorberbaum, J.P., Nahas, Z., Vincent, D.J.: Activation of prefrontal cortex and anterior thalamus in alcoholic subjects on exposure to alcohol-specific case. Arch. Gen. Psychiatry 58, 345–352 (2001)CrossRefGoogle Scholar
  11. 11.
    Lovinger, D.M.: Communication networks in the brain: neurons, receptors, neurotransmitters and alcohol. Alcohol. Res. Health. 31, 196–214 (2008)Google Scholar
  12. 12.
    Nardone, R., Bergmann, J., Kronbichler, M., Caleri, F., Lochner, P., Tezzon, F., Ladurner, G., Golaszewski, S.: Altered motor cortex excitability to magnetic stimulation in alcohol withdrawal syndrome. Alcohol. Clin. Exp. Res. 34, 628–632 (2010)CrossRefGoogle Scholar
  13. 13.
    Lintunen, M., Hyytiä, P., Sallmen, T., Karlstedt, K., Tuomisto, L., Leurs, R., Kiianmaa, K., Korpi, E.R., Panula, P.: Increased brain histamine in an alcohol-preferring rat line and modulation of ethanol consumption by H3 receptor mechanisms. FASEB J. 15, 1074–1076 (2001)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  • Surendra Kumar
    • 1
  • Subhojit Ghosh
    • 2
  • Suhash Tetarway
    • 3
  • Shashank Sawai
    • 4
  • Pillutla Soma Sunder
    • 1
  • Rakesh Kumar Sinha
    • 1
  1. 1.Department of Bio EngineeringBirla Institute of TechnologyMesraIndia
  2. 2.Department of Electrical EngineeringNational Institute of Technology, RaipurRaipurIndia
  3. 3.Department of PhysiologyRajendra Institute of Medical SciencesRanchiIndia
  4. 4.Department of Electrical EngineeringBirla Institute of TechnologyMesraIndia

Personalised recommendations