Shift-Invariant Feature Attribution in the Application of Wireless Electrocardiograms
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 imp...
| Authors | Yalemzerf Getnet, Abiy Tasissa, Waltenegus Dargie |
|---|---|
| Primary category | eess.SP |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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