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An ECG Based CNN Model for Detection of Different Classes of Arrhythmia

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Abstract

Electrocardiograms (ECGs) use electrodes to monitor heart rates and discover tiny electrical changes in each beating. This test can detect abnormal cardiac activity such as arrhythmias and conduction abnormalities. The goal of this research is to offer a method for analyzing and classifying the six micro-classes of cardiac variations found in the MIT-BIH arrhythmia database. Normal (NOR), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature (AP), Premature Ventricular Contraction (PVC), and others (/) are the classifications. The wavelet transform is used to pre-process the input signals, and the peak detection technique is used to identify the R peaks. After preprocessing, oversampling is conducted on the underrepresented classes to improve feature learning by the Convolutional Neural Network(CNN).CNN has 15 levels that are used to process the input. The model achieves a 99.42 accuracy rate.

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Data availability

The data will be made available upon request.

Abbreviations

CNN:

Convolutional neural network

ECG:

Electrocardiogram

NOR/N:

Normal heartbeat

AP/A:

Atrial premature

PVC/V:

Premature ventricular contraction

LBBB/L:

Left bundle branch block

RBBB/R:

Right bundle branch block

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Acknowledgements

The authors would like to thank the Advanced Manufacturing Technology Development Center for valuable scientific discussions.

Funding

This research was conducted without any external funding.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Government Engineering College Raipur, Raipur, Chhattisgarh, 492001, India

    Unnati Mishra & Prakhar Golchha

  2. Defence Reserach and Development Laboratory, Hyderabad, Telangana, 500001, India

    J. John Rozario Jegaraj

  3. Advanced Manufacturing Technology Development Centre, IIT Madras, Chennai, Tamil Nadu, 600001, India

    Omkar Ashok Banne

Authors
  1. Unnati Mishra
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  2. Prakhar Golchha
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  3. J. John Rozario Jegaraj
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  4. Omkar Ashok Banne
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Correspondence to Unnati Mishra or Prakhar Golchha.

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Mishra, U., Golchha, P., Jegaraj, J.J.R. et al. An ECG Based CNN Model for Detection of Different Classes of Arrhythmia. SN COMPUT. SCI. 5, 661 (2024). https://doi.org/10.1007/s42979-024-02951-w

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