Arrhythmic Heartbeat Classification Using 2D Convolutional Neural Networks

Abstract

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.

An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal

“…We have compared our findings with [ 12 , 36 – 39 , 58 , 59 , 61 , 62 ] in Table 11 , where the authors employed almost similar approaches with our works in case of 2D CNN. In the table, we have only placed the results from E 1 and E 2 .…”

ECG Recurrence Plot-Based Arrhythmia Classification Using Two-Dimensional Deep Residual CNN Features

“…They reported 99.02% accuracy for classification using their model, which includes three convolutional layers, two downsampling layers, and a fully connected layer. De girmenci et al [66] classified five types of arrhythmias using a balanced distribution of ECG heartbeat images from the MITDB database, with an overall accuracy of 99.7%. Izci et al [67] reported an accurate arrhythmia detection approach for five different types of arrhythmias that achieved 97.42% accuracy.…”

“…Their accuracy was 97.96%, and their F1-score was 84.94%. In [65,66,68], different algorithms were proposed to classify 2D ECGs with better accuracy than the proposed approach. Although most of them did not specify the segment length, it can be observed that they used shorter segments than the proposed work.…”

Machine Learning to Predict the Antimicrobial Activity of Cold Atmospheric Plasma-Activated Liquids

“…Hyperparameter tuning is the process of searching for the ideal model architecture and its optimum parameters. It is a crucial phase in the model training process that allows the model to test various combinations of hyperparameters and make predictions using the optimal hyperparameter values [70]. For hyperparameter tuning, a variety of techniques are utilized, including grid search, random search, and informed search [71].…”