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Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs

The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registe...

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Autores principales: Santos Rodrigues, Ana, Augustauskas, Rytis, Lukoševičius, Mantas, Laguna, Pablo, Marozas, Vaidotas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9328169/
https://www.ncbi.nlm.nih.gov/pubmed/35891094
http://dx.doi.org/10.3390/s22145414
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author Santos Rodrigues, Ana
Augustauskas, Rytis
Lukoševičius, Mantas
Laguna, Pablo
Marozas, Vaidotas
author_facet Santos Rodrigues, Ana
Augustauskas, Rytis
Lukoševičius, Mantas
Laguna, Pablo
Marozas, Vaidotas
author_sort Santos Rodrigues, Ana
collection PubMed
description The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registered with consumer healthcare devices would, therefore, facilitate ambulatory monitoring. (1) Objective: Develop a method to estimate spatial QRS-T angles from reduced-lead ECGs. (2) Approach: We designed a deep learning model to locate the QRS and T wave vectors necessary for computing the QRS-T angle. We implemented an original loss function to guide the model in the 3D space to search for each vector’s coordinates. A gradual reduction of ECG leads from the largest publicly available dataset of clinical 12-lead ECG recordings (PTB-XL) is used for training and validation. (3) Results: The spatial QRS-T angle can be estimated from leads {I, II, aVF, V2} with sufficient accuracy (absolute mean and median errors of 11.4° and 7.3°) for detecting abnormal angles without sacrificing patient comfortability. (4) Significance: Our model could enable ambulatory monitoring of spatial QRS-T angles using patch- or textile-based ECG devices. Populations at risk of SCD, like chronic cardiac and kidney disease patients, might benefit from this technology.
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spelling pubmed-93281692022-07-28 Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs Santos Rodrigues, Ana Augustauskas, Rytis Lukoševičius, Mantas Laguna, Pablo Marozas, Vaidotas Sensors (Basel) Article The spatial QRS-T angle is a promising health indicator for risk stratification of sudden cardiac death (SCD). Thus far, the angle is estimated solely from 12-lead electrocardiogram (ECG) systems uncomfortable for ambulatory monitoring. Methods to estimate QRS-T angles from reduced-lead ECGs registered with consumer healthcare devices would, therefore, facilitate ambulatory monitoring. (1) Objective: Develop a method to estimate spatial QRS-T angles from reduced-lead ECGs. (2) Approach: We designed a deep learning model to locate the QRS and T wave vectors necessary for computing the QRS-T angle. We implemented an original loss function to guide the model in the 3D space to search for each vector’s coordinates. A gradual reduction of ECG leads from the largest publicly available dataset of clinical 12-lead ECG recordings (PTB-XL) is used for training and validation. (3) Results: The spatial QRS-T angle can be estimated from leads {I, II, aVF, V2} with sufficient accuracy (absolute mean and median errors of 11.4° and 7.3°) for detecting abnormal angles without sacrificing patient comfortability. (4) Significance: Our model could enable ambulatory monitoring of spatial QRS-T angles using patch- or textile-based ECG devices. Populations at risk of SCD, like chronic cardiac and kidney disease patients, might benefit from this technology. MDPI 2022-07-20 /pmc/articles/PMC9328169/ /pubmed/35891094 http://dx.doi.org/10.3390/s22145414 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
Santos Rodrigues, Ana
Augustauskas, Rytis
Lukoševičius, Mantas
Laguna, Pablo
Marozas, Vaidotas
Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs
title Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs
title_full Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs
title_fullStr Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs
title_full_unstemmed Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs
title_short Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs
title_sort deep-learning-based estimation of the spatial qrs-t angle from reduced-lead ecgs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9328169/
https://www.ncbi.nlm.nih.gov/pubmed/35891094
http://dx.doi.org/10.3390/s22145414
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