Atrial fibrillation (AF) is one of the most prevalent cardiac arrhythmias that affects the lives of many people around the world and is associated with a five-fold increased risk of stroke and mortality. Like other problems in the healthcare domain, artificial intelligence (AI)-based models have been used to detect AF from patients’ ECG signals. The cardiologist level performance in detecting this arrhythmia is often achieved by deep learning-based methods, however, they suffer from the lack of interpretability. In other words, these approaches are unable to explain the reasons behind their decisions. The lack of interpretability is a common challenge toward a wide application of machine learning (ML)-based approaches in the healthcare which limits the trust of clinicians in such methods. To address this challenge, we propose HAN-ECG, an interpretable bidirectional-recurrent-neural-network-based approach for the AF detection task. The HAN-ECG employs three attention mechanism levels to provide a multi-resolution analysis of the patterns in ECG leading to AF. The detected patterns by this hierarchical attention model facilitate the interpretation of the neural network decision process in identifying the patterns in the signal which contributed the most to the final detection. Experimental results on two AF databases demonstrate that our proposed model performs better than the existing algorithms. Visualization of these attention layers illustrates that our proposed model decides upon the important waves and heartbeats which are clinically meaningful in the detection task (e.g., absence of P-waves, and irregular R-R intervals for the AF detection task).

心房颤动（AF）是影响全世界许多人生活的最普遍的心律不齐之一，并伴有中风和死亡风险增加五倍。像医疗保健领域中的其他问题一样，基于人工智能（AI）的模型已用于从患者的ECG信号中检测房颤。心脏病专家在检测这种心律失常方面的性能通常是通过基于深度学习的方法来实现的，但是它们缺乏可解释性。换句话说，这些方法无法解释其决定背后的原因。缺乏可解释性是对基于机器学习（ML）的方法在医疗保健中广泛应用的普遍挑战，这限制了临床医生对此类方法的信任。为了应对这一挑战，我们建议HAN-ECG，一种用于AF检测任务的可解释双向双向神经网络方法。在韩心电图运用三个注意机制级别来对导致AF的ECG模式进行多分辨率分析。通过这种分层注意力模型检测到的模式有助于在识别信号中对最终检测贡献最大的模式时解释神经网络决策过程。在两个AF数据库上的实验结果表明，我们提出的模型的性能优于现有算法。这些注意层的可视化表明，我们提出的模型决定了在检测任务中具有临床意义的重要波动和心跳（例如，没有P波，并且对于AF检测任务而言，RR间隔不规则）。