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Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare

Author

Listed:
  • Njud S. Alharbi

    (Department of Biological Sciences, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Hadi Jahanshahi

    (Institute of Electrical and Electronics Engineers, Toronto, ON M5V 3T9, Canada)

  • Qijia Yao

    (School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China)

  • Stelios Bekiros

    (Department of Banking and Finance, FEMA, University of Malta, MSD 2080 Msida, Malta
    LSE Health, Department of Health Policy, London School of Economics and Political Science, London WC2A 2AE, UK
    IPAG Business School, 184, Bd Saint-Germain, 75006 Paris, France)

  • Irene Moroz

    (Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK)

Abstract

In the rapidly evolving field of medical diagnosis, the accurate and prompt interpretation of heartbeat electrocardiogram (ECG) signals have become increasingly crucial. Despite the presence of recent advances, there is an exigent need to enhance the accuracy of existing methodologies, especially given the profound implications such interpretations can have on patient prognosis. To this end, we introduce a novel ensemble comprising Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models to enable the enhanced classification of heartbeat ECG signals. Our approach capitalizes on LSTM’s exceptional sequential data learning capability and CNN’s intricate pattern recognition strength. Advanced signal processing methods are integrated to enhance the quality of raw ECG signals before feeding them into the deep learning model. Experimental evaluations on benchmark ECG datasets demonstrate that our proposed ensemble model surpasses other state-of-the-art deep learning models. It achieves a sensitivity of 94.52%, a specificity of 96.42%, and an accuracy of 95.45%, highlighting its superior performance metrics. This study introduces a promising tool for bolstering cardiovascular disease diagnosis, showcasing the potential of such techniques to advance preventive healthcare.

Suggested Citation

  • Njud S. Alharbi & Hadi Jahanshahi & Qijia Yao & Stelios Bekiros & Irene Moroz, 2023. "Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare," Mathematics, MDPI, vol. 11(18), pages 1-17, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3942-:d:1241614
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    References listed on IDEAS

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    1. Yousefpour, Amin & Jahanshahi, Hadi & Bekiros, Stelios, 2020. "Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    2. Cen, Zhongpei & Wang, Jun, 2019. "Crude oil price prediction model with long short term memory deep learning based on prior knowledge data transfer," Energy, Elsevier, vol. 169(C), pages 160-171.
    3. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
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    Cited by:

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