Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing
  and Knowledge Discovery
Patrick Wagner Temesgen Mehari Wilhelm Haverkamp Nils Strodthoff
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.