Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Mehari, Temesgen"'
Autor:
Mehari, Temesgen, Strodthoff, Nils
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 of such systems. These components consistently enhance performance beyo
Externí odkaz:
http://arxiv.org/abs/2308.15291
Publikováno v:
Computers in Biology and Medicine, Vol. 176, June 2024, 108525
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
Externí odkaz:
http://arxiv.org/abs/2305.17043
Autor:
Mehari, Temesgen, Sundar, Ashish, Bosnjakovic, Alen, Harris, Peter, Williams, Steven E., Loewe, Axel, Doessel, Olaf, Nagel, Claudia, Strodthoff, Nils, Aston, Philip J.
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 disagree and they are inherently difficult to evaluate due to a lack o
Externí odkaz:
http://arxiv.org/abs/2304.02577
Autor:
Mehari, Temesgen, Strodthoff, Nils
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 structured state space models (SSMs) as a particularly promising approach du
Externí odkaz:
http://arxiv.org/abs/2211.07579
Publikováno v:
In Computers in Biology and Medicine June 2024 176
Autor:
Mehari, Temesgen, Strodthoff, Nils
Publikováno v:
Comput. Biol. Med. 141 (2022) 105114
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 remains a central challenge in the field. Self-supervised learning represe
Externí odkaz:
http://arxiv.org/abs/2103.12676
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 (backprop) algorithm, which roughly accounts for 2/3 of the computational c
Externí odkaz:
http://arxiv.org/abs/2004.04729
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Akademický článek
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Autor:
Strodthoff, Nils, Mehari, Temesgen, Nagel, Claudia, Aston, Philip J., Sundar, Ashish, Graff, Claus, Kanters, Jørgen K., Haverkamp, Wilhelm, Dössel, Olaf, Loewe, Axel, Bär, Markus, Schaeffter, Tobias
Publikováno v:
Scientific Data; 5/13/2023, Vol. 10 Issue 1, p1-11, 11p