189 citations · 234 across the 6 of their papers we have counts for
6 papers
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…
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…
ECG Feature Importance Rankings: Cardiologists vs. Algorithms
Temesgen Mehari, Ashish Sundar, Alen Bosnjakovic +7
Feature importance methods promise to provide a ranking of features according to importance for a given classification task. A wide range of methods exist but their rankings often…
Advancing the State-of-the-Art for ECG Analysis through Structured State Space Models
Temesgen Mehari, Nils Strodthoff
The field of deep-learning-based ECG analysis has been largely dominated by convolutional architectures. This work explores the prospects of applying the recently introduced struct…
Self-supervised representation learning from 12-lead ECG data
Temesgen Mehari, Nils Strodthoff
Clinical 12-lead electrocardiography (ECG) is one of the most widely encountered kinds of biosignals. Despite the increased availability of public ECG datasets, label scarcity rema…
Dithered backprop: A sparse and quantized backpropagation algorithm for more efficient deep neural network training
Simon Wiedemann, Temesgen Mehari, Kevin Kepp +1
Deep Neural Networks are successful but highly computationally expensive learning systems. One of the main sources of time and energy drains is the well known backpropagation (back…