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International Conference on Web Information Systems Engineering

WISE 2021: Web Information Systems Engineering – WISE 2021 pp 591–602Cite as

Developing a Deep Learning Based Approach for Anomalies Detection from EEG Data

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

Abstract

Electroencephalography (EEG) contribute a leading role in brain studies, mental and brain diseases and disorders diagnosis, and treatments. Traditional Machine Learning (TML) approaches are employed in most of the recent efforts in identifying the anomalies from EEG data. But their shallowed architecture is one of the reasons why they fail to detect correctly and efficiently. Furthermore, these systems need to be fed the discriminant features manually. To overcome these issues, this study aims to develop an EEG data analysis system involving a multi-layer Gated Recurrent Unit (GRU) for anomalies detection. There are four steps to the suggested framework: (1) Collecting Raw EEG Data, (2) Data pre-processing (de-noising, segmenting, and down-sampling), (3) discover hidden significant characteristics of EEG data and classification using GRU based scheme, and (4) model’s performance evaluation. Our proposed model is tested on a publicly available EEG dataset and achieved 96.91% of accuracy, 97.95% of sensitivity, 96.16% of specificity and 96.39% of F1 score. This study will guide the future bio-medical researchers and technology experts to have a deep learning based automated anomaly detection system from EEG data.

Keywords

  • Gated Recurrent Unit
  • EEG
  • Deep learning
  • Data mining
  • Mild cognitive impairment

Supported by Australian Research Council.

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Authors and Affiliations

  1. Victoria University, Melbourne, VIC, Australia

    Ashik Mostafa Alvi, Siuly Siuly & Hua Wang

Authors
  1. Ashik Mostafa Alvi
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  2. Siuly Siuly
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  3. Hua Wang
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Corresponding author

Correspondence to Ashik Mostafa Alvi .

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

    Prof. Dr. Zakaria Maamar

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

    Dr. Lu Chen

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Alvi, A.M., Siuly, S., Wang, H. (2021). Developing a Deep Learning Based Approach for Anomalies Detection from EEG 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_45

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  • DOI: https://doi.org/10.1007/978-3-030-90888-1_45

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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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