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Res_1D_CNN and BiLSTM with Attention Mechanism Integration for Arrhythmia Diagnosis

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Advances in Computational Collective Intelligence (ICCCI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1864))

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Abstract

An electrocardiogram (ECG) is the most common test used to diagnose an arrhythmia, which arrhythmia is related to abnormal electrical activities of the heart that can be reflected by the ECG which plays the main role in heart disease analysis. However, it is still a challenge to detect arrhythmia based on ECG basic characteristics because of the non-stationary nature of ECG signal even cardiologists faced challenges in arrhythmia diagnosis. Therefore, automatic arrhythmia detection-based ECG signals with height accuracy is a serious and indispensable task. Hence In this paper, we propose a new deep learning-based approach which is a combination of Res 1D CNN for spatial feature extraction and BiLSTM to learn temporal features in two directions with an attention layer to focus only on informative information. A comprehensive experimental study has been made in this research, which shows that the proposed approach offers the most efficient tool for accurate classification and ranks top of the list of recently published algorithms on the MIT-BIH arrhythmia dataset based on the AMII standard. A 5-fold cross-validation is carried out. The proposed model achieved an accuracy, recall, precision, and F1-score of 97.17%, 83.48%, 92.25%, and 87.08%, respectively. The proposed model provides a robust tool for the early detection of arrhythmias.

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Author information

Authors and Affiliations

  1. REGIM-Lab: REsearch Groups in Intelligent Machines, National Engineering School of Sfax (ENIS), University of Sfax, BP 1173, 3038, Sfax, Tunisia

    Wissal Midani & Mounir Ben Ayed

  2. Digital Research Centre of Sfax, Sfax, Tunisia

    Wael Ouarda

  3. Faculty of Economics and Management of Sfax, University of Sfax, Road Airport Km 4, 3018, Sfax, Tunisia

    Wissal Midani

  4. Faculty of Sciences of Sfax, University of Sfax, Road Sokra Km 4, 3000, Sfax, Tunisia

    Mounir Ben Ayed

Authors
  1. Wissal Midani
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  2. Wael Ouarda
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  3. Mounir Ben Ayed
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Corresponding author

Correspondence to Wissal Midani .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Eötvös Loránd University, Budapest, Hungary

    János Botzheim

  3. Eötvös Loránd University, Budapest, Hungary

    László Gulyás

  4. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Nunez

  5. Vrije Universiteit Amsterdam, Amsterdam, The Netherlands

    Jan Treur

  6. University of Münster, Münster, Germany

    Gottfried Vossen

  7. Wrocław University of Science and Technology, Wrocław, Poland

    Adrianna Kozierkiewicz

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Midani, W., Ouarda, W., Ayed, M.B. (2023). Res_1D_CNN and BiLSTM with Attention Mechanism Integration for Arrhythmia Diagnosis. In: Nguyen, N.T., et al. Advances in Computational Collective Intelligence. ICCCI 2023. Communications in Computer and Information Science, vol 1864. Springer, Cham. https://doi.org/10.1007/978-3-031-41774-0_59

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  • DOI: https://doi.org/10.1007/978-3-031-41774-0_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-41773-3

  • Online ISBN: 978-3-031-41774-0

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