Arrhythmic Heartbeat Classification Using 2D Convolutional Neural Networks

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

doi:10.1016/j.irbm.2021.04.002

Abstract

BackgroundElectrocardiogram (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. MethodsThis 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. ResultsThe 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. ConclusionsTest 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.

Highlights

A heartbeat is an event that occurs when the heart contracts and relaxes rhythmically
The Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) database is well-known to be imbalanced by the non-equal number of ECG beats for each arrhythmia which deteriorates the accuracy of Deep Neural Network (DNN) and Convolutional Neural Network (CNN) models [34]
The proposed 2D CNN-based study is separated into two phases; training and testing

Summary

Introduction

A heartbeat is an event that occurs when the heart contracts and relaxes rhythmically. Manual analysis of the ECG signal recordings is not efficient to correctly detect abnormalities in the heart rhythm [4,5]. Many studies in the literature presented some forms of computer-aided systems by using different feature extraction and classification techniques to accurately detect abnormalities in the ECG signals. There have been several methods for automatically detecting arrhythmias based on signal processing, feature extraction, and machine learning algorithms [6]. In [16], time-frequency (TF) analysis of ECG signals is applied in the detection of cardiac arrhythmias. Many studies have developed arrhythmia detection algorithms by using preprocessing, feature extraction, and machine learning techniques, they have limitations for accurately classifying arrhythmias. The proposed approach identifies arrhythmia classes using Convolutional Neural Network (CNN) trained by two-dimensional (2D) ECG beat images.

Methods

Results

Discussion

Conclusion