Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery
This work addresses the problem of model interpretability for clinicians and auditors in ECG analysis, offering incremental improvements through systematic evaluation of existing XAI techniques.
The study tackled the lack of transparency in deep neural networks for ECG analysis by evaluating post-hoc explainable AI methods, establishing sanity checks and providing quantitative evidence aligned with expert rules to enable knowledge discovery like identifying myocardial infarction subtypes.
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.