A CNN-based classifier for detecting rhythm disorders, premature contractions, and conduction abnormalities from ECG

Arrhythmia is a condition characterized by an irregular heart rhythm. An electrocardiogram (ECG) is a widely used technique employed for identifying arrhythmias, as it reveals the morphological changes in the ECG waveform associated with these irregularities. The aim of this work is to classify arrhythmias using a deep learning-based convolutional neural network (CNN) has been introduced that eliminates the need for manual feature extraction. The CNN architecture is carefully designed with a large kernel size to enhance its effectiveness in capturing relevant features. In addition, to this arrhythmia is further subdivided based on their physiological origin of rhythm disorder, premature contraction, and conduction disorder for better feature learning. In order to improve the model’s generalization capability, a dataset classifier is proposed for three publicly available datasets: Chapman, CPSC 2018, and TNMG, each with different class distributions. This classifier is designed to account for variations in test sample distribution and dataset distributions, ensuring the model performs reliably across the datasets used in this study. The architecture is trained and tested on three publicly available datasets Chapman, CPSC 2018, and TNMG each with different class classifications. The CNN model extracts hierarchical features from the Lead II ECG signal. By utilizing publicly available datasets, the performance of the CNN model is evaluated and compared to existing state-of-the-art classification models. Experimental results demonstrate that the CNN model achieved an average F1-score of 98.00%, 96.25%, and 96.50% on the Chapman, CPSC 2018, and TNMG datasets, respectively. Additionally, the model achieved accuracies of 98.13%, 96.30%, and 96.88% on the Chapman, CPSC 2018, and TNMG datasets, respectively, outperforming other current models.