Arrhythmic Heartbeat Classification Using 2D Convolutional Neural Networks

Degirmenci M., Ozdemir M., İzci E., Akan A.

IRBM, vol.43, no.5, pp.422-433, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 43 Issue: 5
  • Publication Date: 2022
  • Doi Number: 10.1016/j.irbm.2021.04.002
  • Journal Name: IRBM
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE
  • Page Numbers: pp.422-433
  • Keywords: Arrhythmia, Classification, Convolutional neural networks, Deep learning, Electrocardiogram
  • Kütahya Health Sciences University Affiliated: No


Background: Electrocardiogram (ECG) is a method of recording the electrical activity of the heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any irregularity of the heartbeat that causes an abnormality in the heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis of ECG recordings is not practical for quickly identifying arrhythmias that may cause sudden deaths. Hence, many studies have been presented to develop computer-aided-diagnosis (CAD) systems to automatically identify arrhythmias. Methods: This paper proposes a novel deep learning approach to identify arrhythmias in ECG signals. The proposed approach identifies arrhythmia classes using Convolutional Neural Network (CNN) trained by two-dimensional (2D) ECG beat images. Firstly, ECG signals, which consist of 5 different arrhythmias, are segmented into heartbeats which are transformed into 2D grayscale images. Afterward, the images are used as input for training a new CNN architecture to classify heartbeats. Results: The experimental results show that the classification performance of the proposed approach reaches an overall accuracy of 99.7%, sensitivity of 99.7%, and specificity of 99.22% in the classification of five different ECG arrhythmias. Further, the proposed CNN architecture is compared to other popular CNN architectures such as LeNet and ResNet-50 to evaluate the performance of the study. Conclusions: Test results demonstrate that the deep network trained by ECG images provides outstanding classification performance of arrhythmic ECG signals and outperforms similar network architectures. Moreover, the proposed method has lower computational costs compared to existing methods and is more suitable for mobile device-based diagnosis systems as it does not involve any complex preprocessing process. Hence, the proposed approach provides a simple and robust automatic cardiac arrhythmia detection scheme for the classification of ECG arrhythmias.