Deep neural networks have become increasingly popular for analyzing ECG data because of their ability to accurately identify cardiac conditions and hidden clinical factors. However, the lack of transparency due to the black box nature of these models is a common concern. To address this issue, explainable AI (XAI) methods can be employed. In this study, we present a comprehensive analysis of post-hoc XAI methods, investigating the local (attributions per sample) and global (based on domain expert concepts) perspectives. We have established a set of sanity checks to identify sensible attribution methods, and we provide quantitative evidence in accordance with expert rules. This dataset-wide analysis goes beyond anecdotal evidence by aggregating data across patient subgroups. Furthermore, we demonstrate how these XAI techniques can be utilized for knowledge discovery, such as identifying subtypes of myocardial infarction. We believe that these proposed methods can serve as building blocks for a complementary assessment of the internal validity during a certification process, as well as for knowledge discovery in the field of ECG analysis.

翻译：深度神经网络因其能够准确识别心脏疾病及隐藏临床因素，在ECG数据分析中日益普及。然而，这些模型的黑箱特性导致可解释性不足，这是常见问题。为解决这一问题，可借助可解释人工智能（XAI）方法。本研究对事后XAI方法进行了综合分析，从局部（每个样本的归因）和全局（基于领域专家概念）视角展开探究。我们建立了一套合理性检查方法以识别合理的归因方法，并提供了符合专家规则的量化证据。这项覆盖整个数据集的分析通过聚合患者亚组数据，超越了基于个例的证据。此外，我们展示了这些XAI技术如何用于知识发现，例如识别心肌梗死的亚型。我们相信，这些方法可作为认证过程中内部有效性补充评估的构建模块，以及ECG分析领域知识发现的基石。