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An Automated Alcoholism Detection Using Orthogonal Wavelet Filter Bank

  • Sunny Shah
  • Manish Sharma
  • Dipankar Deb
  • Ram Bilas Pachori
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

Alcohol misuse is a common social issue related to the central nervous system. Electroencephalogram (EEG) signals are used to depict electrical activities of the brain. In the proposed study, a new computer-aided diagnosis (CAD) has been developed to recognize alcoholic and normal EEG patterns, accurately. In this paper, we present an automatic system for the classification of normal and alcoholic EEG signals using orthogonal wavelet filter bank (OWFB). First, we derive sub-bands (SBs) of EEG signals. Then, we compute logarithms of the energies (LEs) of the SBs. The LEs are employed as the discriminating features for the separation of alcoholic and normal EEG signals. A supervised machine learning algorithm called K nearest neighbor (KNN) has been employed to classify normal and alcoholic patterns. The proposed model has yielded very good classification results. We have achieved a classification accuracy (CA) of 94.20% with tenfold cross-validation (CV).

Keywords

Alcoholism Electroencephalogram (EEG) Ensemble subspace KNN Feature CAD 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Sunny Shah
    • 1
  • Manish Sharma
    • 1
  • Dipankar Deb
    • 1
  • Ram Bilas Pachori
    • 2
  1. 1.Institute of Infrastructure Technology Research and ManagementAhmedabadIndia
  2. 2.Discipline of Electrical EngineeringIndian Institute of Technology IndoreIndoreIndia

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