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An explainable attention-based TCN heartbeats classification model for arrhythmia detection by Yuxuan Zhao; Jiadong Ren; Bing Zhang; Jinxiao Wu; Yongqiang Lyu is a Medicine article available to read on EtoBox.

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Background and Objective: Electrocardiogram (ECG) is a non-invasive tool to measure the heart’s electrical activity. ECG signal based automatic heartbeat classification is a critical task for arrhythmia detection and continues to be challenging. While diverse automated classification methods have been developed, they still cannot provide acceptable performance in classifying different heartbeats because of their poor ability to extract abstract patterns comprehensively. Besides, the performance of previous work drops sharply when dealing with imbalanced datasets and lacks interpretability. Methods: This paper proposes a novel, explainable attention-based temporal convolutional network(TCN) heartbeat classification method. The first contribution of our approach is that we fuse the TCN architecture and self-attention mechanism to encode the ECG heartbeat sequences. Specifically, TCN and the self-attention block are designed to capture global variation tends and local features, respectively, to best serve the classification. Meanwhile, multi-class focal loss helps model training overcome the class imbalance problem. In the end, the dynamic perturbation based high-fidelity explanation

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Author

Yuxuan Zhao; Jiadong Ren; Bing Zhang; Jinxiao Wu; Yongqiang Lyu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Physical Sciences)