Multi-scale Feature Learning with CNN-RNN-Attention Framework for ECG-based Cancer Therapy-Related Cardiac...

Cancer therapy-related cardiac dysfunction (CTRCD) is an increasingly significant concern due to cardiac function deterioration caused by anticancer drug side effects. While echocardiography is the conventional diagnostic method for CTRCD, its accuracy heavily depends on operator expertise and the procedure is both time-consuming and costly. Electrocardiogram (ECG), being more accessible and easier to measure, presents a promising lower-cost alternative. In this paper, we propose a deep learning model for CTRCD detection from ECG signals. Our model integrates convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to capture both local and global ECG features, while incorporating an attention mechanism to comprehensively learn feature importance. To enhance model interpretability, we visualize the attention weights to identify ECG features that significantly contribute to the classification decision. Through extensive ablation studies using standard 12-lead ECG data, we demonstrate the effectiveness of our proposed architecture. This work is expected to contribute to the development of cost-effective and reliable diagnostic tools for monitoring cardiac side effects during cancer treatment.