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Deep Generative Models: The winning key for large and easily accessible ECG datasets?
- Source :
-
Computers in biology and medicine [Comput Biol Med] 2023 Dec; Vol. 167, pp. 107655. Date of Electronic Publication: 2023 Nov 02. - Publication Year :
- 2023
-
Abstract
- Large high-quality datasets are essential for building powerful artificial intelligence (AI) algorithms capable of supporting advancement in cardiac clinical research. However, researchers working with electrocardiogram (ECG) signals struggle to get access and/or to build one. The aim of the present work is to shed light on a potential solution to address the lack of large and easily accessible ECG datasets. Firstly, the main causes of such a lack are identified and examined. Afterward, the potentials and limitations of cardiac data generation via deep generative models (DGMs) are deeply analyzed. These very promising algorithms have been found capable not only of generating large quantities of ECG signals but also of supporting data anonymization processes, to simplify data sharing while respecting patients' privacy. Their application could help research progress and cooperation in the name of open science. However several aspects, such as a standardized synthetic data quality evaluation and algorithm stability, need to be further explored.<br />Competing Interests: Declaration of competing interest None Declared<br /> (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
- Subjects :
- Humans
Heart
Algorithms
Data Accuracy
Artificial Intelligence
Electrocardiography
Subjects
Details
- Language :
- English
- ISSN :
- 1879-0534
- Volume :
- 167
- Database :
- MEDLINE
- Journal :
- Computers in biology and medicine
- Publication Type :
- Academic Journal
- Accession number :
- 37976830
- Full Text :
- https://doi.org/10.1016/j.compbiomed.2023.107655