Classification of ECG signals using CNN models

Abstract Electrical activity is essential for blood circulation, and any alteration in the orderly pattern of excitation waves propagating through the heart can lead to arrhythmias. The electrocardiogram (ECG) is a widely used tool for diagnosing arrhythmias, as it is rapid, affordable, and non-invasive. However, manual interpretation of ECG data is often time-consuming and subject to human error. With improved training, deep learning (DL) could provide a more effective alternative for rapid, automatic classification. This study presents a novel deep learning architecture, including a convolutional neural network (CNN), for classifying cardiac arrhythmias. Real ECG signals from the Heartbeat Categorization ECG dataset were used for training and validation. The models’ performance was evaluated using the confusion matrix, which determined precision, accuracy, recall, F1 score, mean, and AUC-ROC. Both models demonstrated exceptional performance, achieving accuracies of 0.995 and 0.996 on training and test datasets, outperforming existing methods in terms of accuracy and efficiency.