Shift-Invariant Feature Attribution in the Application of Wireless Electrocardiograms
This work addresses the need for interpretable feature attribution in biomedical applications, specifically for ECG analysis to aid medical experts, but it is incremental as it builds on existing attribution methods with domain-specific adaptations.
The paper tackles the problem of assigning interpretable relevance scores to ECG features for explainable machine learning models, proposing a shift-invariant baseline and aggregation method that maps scores to cardiac phases, and demonstrates that P and T waves contribute most to accurate physical exertion recognition.
Assigning relevance scores to the input features of a machine learning model enables to measure the contributions of the features in achieving a correct outcome. It is regarded as one of the approaches towards developing explainable models. For biomedical assignments, this is very useful for medical experts to comprehend machine-based decisions. In the analysis of electro cardiogram (ECG) signals, in particular, understanding which of the electrocardiogram samples or features contributed most for a given decision amounts to understanding the underlying cardiac phases or conditions the machine tries to explain. For the computation of relevance scores, determining the proper baseline is important. Moreover, the scores should have a distribution which is at once intuitive to interpret and easy to associate with the underline cardiac reality. The purpose of this work is to achieve these goals. Specifically, we propose a shift-invariant baseline which has a physical significance in the analysis as well as interpretation of electrocardiogram measurements. Moreover, we aggregate significance scores in such a way that they can be mapped to cardiac phases. We demonstrate our approach by inferring physical exertion from cardiac exertion using a residual network. We show that the ECG samples which achieved the highest relevance scores (and, therefore, which contributed most to the accurate recognition of the physical exertion) are those associated with the P and T waves. Index Terms Attribution, baseline, cardiovascular diseases, electrocardiogram, activity recognition, machine learning