Background and objectiveDetecting abnormal patterns within an electrocardiogram (ECG) is crucial for diagnosing cardiovascular diseases. We start from two unresolved problems in applying deep-learning-based ECG classification models to clinical practice: first, although multiple cardiac arrhythmia (CA) types may co-occur in real life, the majority of previous detection methods have focused on one-to-one relationships between ECG and CA type, and second, it has been difficult to explain how neural-network-based CA classifiers make decisions. We hypothesize that fine-tuning attention maps with regard to all possible combinations of ground-truth (GT) labels will improve both the detection and interpretability of co-occurring CAs.

Methods To test our hypothesis, we propose an end-to-end convolutional neural network (CNN), xECGNet, that fine-tunes the attention map to resemble the averaged response maps of GT labels. Fine-tuning is achieved by adding to the objective function a regularization loss between the attention map and the reference (averaged) map. Performance is assessed by F1 score and subset accuracy.

Results The main experiment demonstrates that fine-tuning alone significantly improves a model’s multilabel subset accuracy from 75.8% to 84.5% when compared with the baseline model. Also, xECGNet shows the highest F1 score of 0.812 and yields a more explainable map that encompasses multiple CA types, when compared to other baseline methods.

Conclusions xECGNet has implications in that it tackles the two obstacles for the clinical application of CNN-based CA detection models with a simple solution of adding one additional term to the objective function.

背景和目的检测心电图 (ECG) 中的异常模式对于诊断心血管疾病至关重要。我们从将基于深度学习的 ECG 分类模型应用于临床实践中的两个未解决的问题开始：首先，尽管现实生活中可能同时发生多种心律失常 (CA) 类型，但以前的大多数检测方法都集中在一对一- ECG 和 CA 类型之间的关系，其次，很难解释基于神经网络的 CA 分类器如何做出决策。我们假设，关于真实 (GT) 标签的所有可能组合的微调注意力图将提高共现 CA 的检测和可解释性。

方法为了检验我们的假设，我们提出了一个端到端的卷积神经网络 (CNN) xECGNet，它可以微调注意力图以类似于 GT 标签的平均响应图。微调是通过向目标函数添加注意力图和参考（平均）图之间的正则化损失来实现的。性能由 F1 分数和子集准确性评估。

结果主要实验表明，与基线模型相比，单独的微调可以将模型的多标签子集准确度从 75.8% 显着提高到 84.5%。此外，与其他基线方法相比，xECGNet 显示了 0.812 的最高 F1 分数，并产生了更可解释的地图，其中包含多种 CA 类型。

结论xECGNet 的意义在于它解决了基于 CNN 的 CA 检测模型的临床应用的两个障碍，通过向目标函数添加一个附加项的简单解决方案。