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Deep Learning-Based Arrhythmia Detection Using RR-Interval Framed Electrocardiograms

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Proceedings of Eighth International Congress on Information and Communication Technology (ICICT 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 694))

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Abstract

Deep learning applied to electrocardiogram (ECG) data can be used to achieve personal authentication in biometric security applications, but it has not been widely used to diagnose cardiovascular disorders. We developed a deep learning model for the detection of arrhythmia in which time-sliced ECG data representing the distance between successive R-peaks are used as the input for a convolutional neural network (CNN). The main objective is developing the compact deep learning-based detect system which minimally uses the dataset but delivers the confident accuracy rate of the arrhythmia detection. This compact system can be implemented in wearable devices or real-time monitoring equipment because the feature extraction step is not required for complex ECG waveforms, only the R-peak data is needed. The 10 hidden layers of the CNN detect arrhythmias using a novel RR-interval framing (RRIF) approach. Two testing processes were implemented, the first during the training and validation of the CNN algorithm and the second using different datasets for testing under realistic conditions. The results of both tests indicated that the Compact Arrhythmia Detection System (CADS) matched the performance of conventional systems for the detection of arrhythmia in two consecutive test runs.

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Author information

Authors and Affiliations

  1. Faculty of Applied Sciences, Macao Polytechnic University, Macau, Macao

    Song-Kyoo Kim

  2. Center for Cyber-Physical Systems (C2PS), Khalifa University, Abu Dhabi, UAE

    Chan Yeob Yeun & Ernesto Damiani

  3. Department of EECS, Khalifa University, Abu Dhabi, UAE

    Chan Yeob Yeun & Ernesto Damiani

  4. CSIS, Birkbeck College, University of London, Malet Street, London, WC1E 7HX, UK

    Paul D. Yoo

  5. Cranfield School of Defence and Sec, Defence Academy of the UK, Shrivenham, UK

    Paul D. Yoo

  6. Department of Info. Mgt, NTUST, Taipei, Taiwan

    Nai-Wei Lo

Authors
  1. Song-Kyoo Kim
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  2. Chan Yeob Yeun
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  3. Paul D. Yoo
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  4. Nai-Wei Lo
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  5. Ernesto Damiani
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Corresponding author

Correspondence to Song-Kyoo Kim .

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Editors and Affiliations

  1. Department of Design Engineering and Mathematics, Middlesex University London, London, UK

    Xin-She Yang

  2. Department of Biomedical Engineering, University of Reading, England, UK

    R. Simon Sherratt

  3. Department of Computer Science and Engineering, Techno International Newtown, Chakpachuria, West Bengal, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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Kim, SK., Yeun, C.Y., Yoo, P.D., Lo, NW., Damiani, E. (2023). Deep Learning-Based Arrhythmia Detection Using RR-Interval Framed Electrocardiograms. In: Yang, XS., Sherratt, R.S., Dey, N., Joshi, A. (eds) Proceedings of Eighth International Congress on Information and Communication Technology. ICICT 2023. Lecture Notes in Networks and Systems, vol 694. Springer, Singapore. https://doi.org/10.1007/978-981-99-3091-3_2

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  • DOI: https://doi.org/10.1007/978-981-99-3091-3_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3090-6

  • Online ISBN: 978-981-99-3091-3

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