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A New Deep Learning Method with Self-Supervised Learning for Delineation of the Electrocardiogram

Heartbeat characteristic points are the main features of an electrocardiogram (ECG), which can provide important information for ECG-based cardiac diagnosis. In this manuscript, we propose a self-supervised deep learning framework with modified Densenet to detect ECG characteristic points, including...

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Autores principales: Wu, Wenwen, Huang, Yanqi, Wu, Xiaomei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778283/
https://www.ncbi.nlm.nih.gov/pubmed/36554233
http://dx.doi.org/10.3390/e24121828
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author Wu, Wenwen
Huang, Yanqi
Wu, Xiaomei
author_facet Wu, Wenwen
Huang, Yanqi
Wu, Xiaomei
author_sort Wu, Wenwen
collection PubMed
description Heartbeat characteristic points are the main features of an electrocardiogram (ECG), which can provide important information for ECG-based cardiac diagnosis. In this manuscript, we propose a self-supervised deep learning framework with modified Densenet to detect ECG characteristic points, including the onset, peak and termination points of P-wave, QRS complex wave and T-wave. We extracted high-level features of ECG heartbeats from the QT Database (QTDB) and two other larger datasets, MIT-BIH Arrhythmia Database (MITDB) and MIT-BIH Normal Sinus Rhythm Database (NSRDB) with no human-annotated labels as pre-training. By applying different transformations to ECG signals, the task of discriminating signals before and after transformation was defined as the pretext task. Subsequently, the convolutional layer was frozen and the weights of the self-supervised network were transferred to the downstream task of characteristic point localizations on heart beats in the QT dataset. Finally, the mean ± standard deviation of the detection errors of our proposed self-supervised learning method in QTDB for detecting the onset, peak, and termination points of P-waves, the onset and termination points of QRS waves, and the peak and termination points of T-waves were −0.24 ± 10.04, −0.48 ± 11.69, −0.28 ± 10.19, −3.72 ± 8.18, −4.12 ± 13.54, −0.68 ± 20.42, and 1.34 ± 21.04. The results show that the deep learning network based on the self-supervised framework constructed in this manuscript can accurately detect the feature points of a heartbeat, laying the foundation for automatic extraction of key information related to ECG-based diagnosis.
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spelling pubmed-97782832022-12-23 A New Deep Learning Method with Self-Supervised Learning for Delineation of the Electrocardiogram Wu, Wenwen Huang, Yanqi Wu, Xiaomei Entropy (Basel) Article Heartbeat characteristic points are the main features of an electrocardiogram (ECG), which can provide important information for ECG-based cardiac diagnosis. In this manuscript, we propose a self-supervised deep learning framework with modified Densenet to detect ECG characteristic points, including the onset, peak and termination points of P-wave, QRS complex wave and T-wave. We extracted high-level features of ECG heartbeats from the QT Database (QTDB) and two other larger datasets, MIT-BIH Arrhythmia Database (MITDB) and MIT-BIH Normal Sinus Rhythm Database (NSRDB) with no human-annotated labels as pre-training. By applying different transformations to ECG signals, the task of discriminating signals before and after transformation was defined as the pretext task. Subsequently, the convolutional layer was frozen and the weights of the self-supervised network were transferred to the downstream task of characteristic point localizations on heart beats in the QT dataset. Finally, the mean ± standard deviation of the detection errors of our proposed self-supervised learning method in QTDB for detecting the onset, peak, and termination points of P-waves, the onset and termination points of QRS waves, and the peak and termination points of T-waves were −0.24 ± 10.04, −0.48 ± 11.69, −0.28 ± 10.19, −3.72 ± 8.18, −4.12 ± 13.54, −0.68 ± 20.42, and 1.34 ± 21.04. The results show that the deep learning network based on the self-supervised framework constructed in this manuscript can accurately detect the feature points of a heartbeat, laying the foundation for automatic extraction of key information related to ECG-based diagnosis. MDPI 2022-12-15 /pmc/articles/PMC9778283/ /pubmed/36554233 http://dx.doi.org/10.3390/e24121828 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Wu, Wenwen
Huang, Yanqi
Wu, Xiaomei
A New Deep Learning Method with Self-Supervised Learning for Delineation of the Electrocardiogram
title A New Deep Learning Method with Self-Supervised Learning for Delineation of the Electrocardiogram
title_full A New Deep Learning Method with Self-Supervised Learning for Delineation of the Electrocardiogram
title_fullStr A New Deep Learning Method with Self-Supervised Learning for Delineation of the Electrocardiogram
title_full_unstemmed A New Deep Learning Method with Self-Supervised Learning for Delineation of the Electrocardiogram
title_short A New Deep Learning Method with Self-Supervised Learning for Delineation of the Electrocardiogram
title_sort new deep learning method with self-supervised learning for delineation of the electrocardiogram
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778283/
https://www.ncbi.nlm.nih.gov/pubmed/36554233
http://dx.doi.org/10.3390/e24121828
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