The necessity of multiple data sources for ECG-based machine learning models

  • Even though the interest in machine learning studies is growing significantly, especially in medicine, the imbalance between study results and clinical relevance is more pronounced than ever. The reasons for this include data quality and interoperability issues. Hence, we aimed at examining site- and study-specific differences in publicly available standard electrocardiogram (ECG) datasets, which in theory should be interoperable by consistent 12-lead definition, sampling rate, and measurement duration. The focus lies upon the question of whether even slight study peculiarities can affect the stability of trained machine learning models. To this end, the performances of modern network architectures as well as unsupervised pattern detection algorithms are investigated across different datasets. Overall, this is intended to examine the generalization of machine learning results of single-site ECG studies.

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Metadaten
Author:Lucas Plagwitz, Tobias VogelsangGND, Florian Doldi, Lucas Bickmann, Michael Fujarski, Lars Eckardt, Julian Varghese
URN:urn:nbn:de:bvb:384-opus4-1164382
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/116438
ISBN:9781643683881OPAC
ISBN:9781643683898OPAC
ISSN:0926-9630OPAC
ISSN:1879-8365OPAC
Parent Title (English):Caring is sharing – exploiting the value in data for health and innovation
Publisher:IOS Press
Place of publication:Amsterdam
Editor:Maria Hägglund, Madeleine Blusi, Stefano Bonacina, Lina Nilsson, Inge Cort Madsen, Sylvia Pelayo, Anne Moen, Arriel Benis, Lars Lindsköld, Parisis Gallos
Type:Part of a Book
Language:English
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2024/11/07
First Page:33
Last Page:37
Series:Studies in Health Technology and Informatics ; 302
DOI:https://doi.org/10.3233/shti230059
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Professur für Diagnostische Sensorik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY-NC 4.0: Creative Commons: Namensnennung - Nicht kommerziell (mit Print on Demand)

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