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.

本研究提出了深度学习及可解释 AI 方法结合，从局部和全局两个角度对心电图数据进行分析，提高了模型的透明度，同时发现了心肌梗塞亚型等心脏疾病的关键特征，为内部审核和疾病研究提供了有力工具。