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20202023
most citedAdvancing the State-of-the-Art for ECG Analysis through Structured State Space Models

5 citations · 8 across the 4 of their papers we have counts for

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eess.SP2023★ 1 cited

Towards quantitative precision for ECG analysis: Leveraging state space models, self-supervision and patient metadata

Temesgen Mehari, Nils Strodthoff

Deep learning has emerged as the preferred modeling approach for automatic ECG analysis. In this study, we investigate three elements aimed at improving the quantitative accuracy o…

eess.SP2023

Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge Discovery

Patrick Wagner, Temesgen Mehari, Wilhelm Haverkamp +1

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…