Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153414
Title: Improving the efficacy of deep-learning models for heart beat detection on heterogeneous datasets
Authors: Bizzego, Andrea
Gabrieli, Giulio
Neoh, Michelle Jin-Yee
Esposito, Gianluca
Keywords: Science::Biological sciences::Human anatomy and physiology
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2021
Source: Bizzego, A., Gabrieli, G., Neoh, M. J. & Esposito, G. (2021). Improving the efficacy of deep-learning models for heart beat detection on heterogeneous datasets. Bioengineering, 8(12), 193-. https://dx.doi.org/10.3390/bioengineering8120193
Journal: Bioengineering
Abstract: Deep learning (DL) has greatly contributed to bioelectric signal processing, in particular to extract physiological markers. However, the efficacy and applicability of the results proposed in the literature is often constrained to the population represented by the data used to train the models. In this study, we investigate the issues related to applying a DL model on heterogeneous datasets. In particular, by focusing on heart beat detection from electrocardiogram signals (ECG), we show that the performance of a model trained on data from healthy subjects decreases when applied to patients with cardiac conditions and to signals collected with different devices. We then evaluate the use of transfer learning (TL) to adapt the model to the different datasets. In particular, we show that the classification performance is improved, even with datasets with a small sample size. These results suggest that a greater effort should be made towards the generalizability of DL models applied on bioelectric signals, in particular, by retrieving more representative datasets.
URI: https://hdl.handle.net/10356/153414
ISSN: 2306-5354
DOI: 10.3390/bioengineering8120193
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:LKCMedicine Journal Articles
SSS Journal Articles

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