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A Novel Transformer-Based Anomaly Detection Approach for ECG Monitoring Healthcare System

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The Seventh International Conference on Safety and Security with IoT (SaSeIoT 2023)

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

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Abstract

Anomaly detection plays a crucial role across various domains, including healthcare, where identifying deviations from normal patterns can lead to early intervention and improved outcomes. In healthcare, such as in ECG analysis, detecting anomalous signals is essential for timely diagnosis and treatment, as it can help identify potentially life-threatening conditions that might otherwise go unnoticed. In this work, by focusing on ECG anomaly detection as an illustrative healthcare application, we propose to use a transformer-based variational autoencoder network together with a MEWMA-SVDD control chart to achieve anomaly detection. By employing this approach, we can effectively control the false alarm rate, aligning with the intended goal of minimizing unnecessary alerts. Our proposed framework not only excels in terms of accuracy but also reduces the false alarm rate, making it a favorable choice compared to existing methods.

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Author information

Authors and Affiliations

  1. HEC Liège - Management School of the University of Liège, Liège, Belgium

    Thi Thuy Van Nguyen

  2. University of Lille, ENSAIT, GEMTEX, Lille, France

    Thi Thuy Van Nguyen

  3. HEC Liège - Management School of the University of Liège, Liège, Belgium

    Cédric Heuchenne

  4. International Research Institute for Artificial Intelligence and Data Science, Dong A University, Danang, Vietnam

    Kim Duc Tran

  5. University of Lille, ENSAIT, GEMTEX, Lille, France

    Kim Phuc Tran

Authors
  1. Thi Thuy Van Nguyen
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  2. Cédric Heuchenne
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  3. Kim Duc Tran
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  4. Kim Phuc Tran
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Corresponding author

Correspondence to Thi Thuy Van Nguyen .

Editor information

Editors and Affiliations

  1. GEMTEX & ENSAIT Graduate School MADIS-631, University of Lille, ROUBAIX CEDEX 1, France

    Kim Phuc Tran

  2. University of Kent, Canterbury, UK

    Shujun Li

  3. University of Liège, Liège, Belgium

    Cédric Heuchenne

  4. Hanoi University of Science and Technology, Hanoi, Vietnam

    Thu Huong Truong

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Nguyen, T.T.V., Heuchenne, C., Tran, K.D., Tran, K.P. (2024). A Novel Transformer-Based Anomaly Detection Approach for ECG Monitoring Healthcare System. In: Tran, K.P., Li, S., Heuchenne, C., Truong, T.H. (eds) The Seventh International Conference on Safety and Security with IoT. SaSeIoT 2023. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-53028-9_7

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  • DOI: https://doi.org/10.1007/978-3-031-53028-9_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53027-2

  • Online ISBN: 978-3-031-53028-9

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