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International Conference on Web Information Systems Engineering

WISE 2021: Web Information Systems Engineering – WISE 2021 pp 198–206Cite as

Data Mining Based Artificial Intelligent Technique for Identifying Abnormalities from Brain Signal Data

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13080))

Abstract

Analysis of brain signal data like Electroencephalography (EEG) plays an important role in efficient diagnosis of neurological disorders and treatment. EEG records electrical activity of the brain and contains huge volume of multi-channel time-series data that are visually analyzed by neurologists to identify abnormalities within the brain, which is time-consuming, error-prone, and subject to fatigue. Therefore, an automatic data mining system is always in need to detect abnormality from those large volume of data. To meet the requirements, in this study, a time-frequency spectrogram image-based classification framework is developed using texture feature extractor and machine learning (ML) based classifiers. At first, signals are filtered to remove noises and artifacts and normalized. Then signals are segmented into small chunks and spectrogram images are generated from those segments using short-time Fourier transform. After that, histogram based textural features are extracted and significant features are selected using principal component analysis. Finally, those features are fed into three ML based classifiers for categorizing the signals into different groups. The proposed system is tested on EEG brain signal data and have obtained promising results in identifying different abnormality groups, which indicates that the proposed system can be used for mining large volume of brain signal data.

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Author information

Authors and Affiliations

  1. Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia

    Md. Nurul Ahad Tawhid, Siuly Siuly & Hua Wang

  2. School of Health and Biomedical Sciences, RMIT University, Melbourne, Australia

    Kate Wang

Authors
  1. Md. Nurul Ahad Tawhid
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  2. Siuly Siuly
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  3. Kate Wang
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  4. Hua Wang
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Corresponding author

Correspondence to Md. Nurul Ahad Tawhid .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia

    Wenjie Zhang

  2. Peking University, Beijing, China

    Lei Zou

  3. Zayed University, Dubai, United Arab Emirates

    Zakaria Maamar

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

    Lu Chen

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Tawhid, M.N.A., Siuly, S., Wang, K., Wang, H. (2021). Data Mining Based Artificial Intelligent Technique for Identifying Abnormalities from Brain Signal Data. In: Zhang, W., Zou, L., Maamar, Z., Chen, L. (eds) Web Information Systems Engineering – WISE 2021. WISE 2021. Lecture Notes in Computer Science(), vol 13080. Springer, Cham. https://doi.org/10.1007/978-3-030-90888-1_16

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  • DOI: https://doi.org/10.1007/978-3-030-90888-1_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90887-4

  • Online ISBN: 978-3-030-90888-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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