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ResNet-50-CNN and LSTM Based Arrhythmia Detection Model Based on ECG Dataset

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Enabling Person-Centric Healthcare Using Ambient Assistive Technology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1108))

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Abstract

The ECG is a critical component of computer-aided arrhythmia detection systems since it helps to reduce the rise in the death rate from disorders of the circulatory system. However, due to the intricate changes and imbalance of electrocardiogram beats, this is a difficult problem to solve. This study provides an innovative and enhanced ResNet-50 model using a Conv-1D model with Long Short Term Memory (LSTM) based on Convolution Neural Network (CNN) approach for arrhythmia identification using ECG data, including proper parameter optimization and model training. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrates that the model performs better, having an accuracy of 98.7% and a MSE of 0.06 when compared to other classification methods.

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Authors and Affiliations

  1. School of Electrical Engineering, Vellore Institute of Technology, Chennai, India

    Ojaswa Yadav, Ayush Singh, Aman Sinha, Chirag Vinit Garg & P. Sriramalakshmi

Authors
  1. Ojaswa Yadav
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  2. Ayush Singh
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  3. Aman Sinha
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  4. Chirag Vinit Garg
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  5. P. Sriramalakshmi
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Corresponding author

Correspondence to P. Sriramalakshmi .

Editor information

Editors and Affiliations

  1. National Research Council, Institute of Information Science and Technologies, Pisa, Italy

    Paolo Barsocchi

  2. Department of Computer Science and Engineering, Prasad V Potluri Siddhartha Institute of Technology, Vijayawada, India

    Naga Srinivasu Parvathaneni

  3. KIET Group of Institutions, Ghaziabad, India

    Amik Garg

  4. KIET Group of Institutions, Ghaziabad, India

    Akash Kumar Bhoi

  5. National Research Council, Institute of Information Science and Technologies, Pisa, Italy

    Filippo Palumbo

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Yadav, O., Singh, A., Sinha, A., Garg, C.V., Sriramalakshmi, P. (2023). ResNet-50-CNN and LSTM Based Arrhythmia Detection Model Based on ECG Dataset. In: Barsocchi, P., Parvathaneni, N.S., Garg, A., Bhoi, A.K., Palumbo, F. (eds) Enabling Person-Centric Healthcare Using Ambient Assistive Technology. Studies in Computational Intelligence, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-031-38281-9_8

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