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Auto-correlation Based Feature Extraction Approach for EEG Alcoholism Identification

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Health Information Science (HIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13079))

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Abstract

Alcoholism severely affects brain functions. Most doctors and researchers utilized Electroencephalogram (EEG) signals to measure and record brain activities. The recorded EEG signals have non-linear and nonstationary attributes with very low amplitude. Consequently, it is very difficult and time-consuming for humans to interpret such signals. Therefore, with the significance of computerized approaches, the identification of normal and alcohol EEG signals has become very useful in the medical field. In the present work, a computer-aided diagnosis (CAD) system is recommended for characterization of normal vs alcoholic EEG signals with following tasks. First, dataset is segmented into several EEG signals. Second, the autocorrelation of each signal is computed to enhance the strength of EEG signals. Third, coefficients of autocorrelation with several lags are considered as features and verified statistically. At last, significant features are tested on twenty machine learning classifiers available in the WEKA platform by employing a 10-fold cross-validation strategy for the classification of normal vs alcoholic signals. The obtained results are effective and support the usefulness of autocorrelation coefficients as features.

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Author information

Authors and Affiliations

  1. School of Automation, Northwestern Polytechnical University, Xi’an, China

    Muhammad Tariq Sadiq

  2. Department of Electrical Engineering, The University of Lahore, Lahore, Pakistan

    Muhammad Tariq Sadiq

  3. Victoria University, Melbourne, VIC, 3011, Australia

    Siuly Siuly & Hua Wang

  4. Government College University, Lahore, 54500, Pakistan

    Ateeq Ur Rehman

Authors
  1. Muhammad Tariq Sadiq
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  2. Siuly Siuly
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  3. Ateeq Ur Rehman
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  4. Hua Wang
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Corresponding author

Correspondence to Muhammad Tariq Sadiq .

Editor information

Editors and Affiliations

  1. Victoria University, Footscray, VIC, Australia

    Siuly Siuly

  2. Victoria University, Footscray, VIC, Australia

    Hua Wang

  3. Swinburne University of Technology, Melbourne, VIC, Australia

    Lu Chen

  4. University of Illinois at Springfield, Springfield, NY, USA

    Yanhui Guo

  5. Tsinghua University, Beijing, China

    Chunxiao Xing

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Sadiq, M.T., Siuly, S., Ur Rehman, A., Wang, H. (2021). Auto-correlation Based Feature Extraction Approach for EEG Alcoholism Identification. In: Siuly, S., Wang, H., Chen, L., Guo, Y., Xing, C. (eds) Health Information Science. HIS 2021. Lecture Notes in Computer Science(), vol 13079. Springer, Cham. https://doi.org/10.1007/978-3-030-90885-0_5

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  • DOI: https://doi.org/10.1007/978-3-030-90885-0_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90884-3

  • Online ISBN: 978-3-030-90885-0

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