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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9778283 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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