CAT-Net: Convolution, attention, and transformer based network for single-lead ECG arrhythmia classification

Abstract

Machine learning technologies have been applied extensively in the last decade to automatically detect and analyze various forms of arrhythmia from electrocardiogram (ECG) signals. Existing deep learning-based models focus on enhancing classification performance by exploring spatial–temporal ECG features or by implementing multi-modal and ensemble classifiers. Such approaches perform well but do not provide comprehensive accessibility for real-life applications due to the multi-lead ECG requirement. To address the issue, a single-lead ECG based network is considered. The proposed convolution, attention, and transformer-based network (CAT-Net) exhibits promising performance on arrhythmia classification by adeptly capturing local and global heartbeats’ morphological characteristics. Along with the local information captured by the convolution layer, the contextual ECG information is extracted by the multi-head attention layer present in the transformer encoder. In addition, most of the existing models suffer from lower predictive performance in minority-class arrhythmias due to the highly imbalanced ECG data. To enhance the predictive performance in minority classes, three balancing techniques – SMOTE-ENN, SMOTE-Tomek, and ADASYN – are systematically evaluated, and SMOTE-Tomek is ultimately integrated. To mitigate potential dataset bias, CAT-Net was assessed across two distinct datasets: MIT-BIH and INCART, respectively, and was shown to achieve state-of-the-art performance. CAT-Net establishes new benchmarks, achieving 99.14% overall accuracy and 94.69% macro F1 score on 5-class arrhythmia classification in the MIT-BIH dataset and 99.58% accuracy with 96.15% macro F1 score for 3-class classification in the INCART dataset.