Exploring the Possibility of Arrhythmia Interpretation of Time Domain ECG Using XAI: A Preliminary Study

Recent advancements in deep learning have spurred active research in arrhythmia detection and automatic electrocardiogram (ECG) labeling. However, the labels provided by deep learning often lack transparent justification, making real-world applications challenging. Here, we utilized signals from a portable single-lead ECG device to classify eight arrhythmia classes with convolutional neural networks (CNN), achieving 79.91% accuracy with a single heartbeat. Additionally, employing the layer-wise relevance propagation (LRP), explainable artificial intelligence (XAI) algorithm, we analyzed the distinctive features of each arrhythmia, particularly focusing on significant differences in the heartbeat. Our XAI approach revealed strong activation in the QRS complex and T-wave for VPC, consistent with medical interpretations that wide QRS complex and an opposite direction of large T-wave in VPC. This research contributes to interpretable deep learning for arrhythmia diagnosis, potentially enhancing decision support systems and biomarker discovery in arrhythmia management. Further exploration of arrhythmia biomarkers is anticipated.