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ECGAssess: A Python-Based Toolbox to Assess ECG Lead Signal Quality
Electrocardiography (ECG) is the method most often used to diagnose cardiovascular diseases. To obtain a high-quality recording, the person conducting an ECG must be a trained expert. When these experts are not available, this important diagnostic tool cannot be used, consequently impacting the qual...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9120362/ https://www.ncbi.nlm.nih.gov/pubmed/35601886 http://dx.doi.org/10.3389/fdgth.2022.847555 |
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author | Kramer, Linus Menon, Carlo Elgendi, Mohamed |
author_facet | Kramer, Linus Menon, Carlo Elgendi, Mohamed |
author_sort | Kramer, Linus |
collection | PubMed |
description | Electrocardiography (ECG) is the method most often used to diagnose cardiovascular diseases. To obtain a high-quality recording, the person conducting an ECG must be a trained expert. When these experts are not available, this important diagnostic tool cannot be used, consequently impacting the quality of healthcare. To avoid this problem, it must be possible for untrained healthcare professionals to record diagnostically useful ECGs so they can send the recordings to experts for diagnosis. The ECGAssess Python-based toolbox developed in this study provides feedback regarding whether ECG signals are of adequate quality. Each lead of the 12-lead recordings was classified as acceptable or unacceptable. This feedback allows people to identify and correct errors in the use of the ECG device. The toolbox classifies the signals according to stationary, heart rate, and signal-to-noise ratio. If the limits of these three criteria are exceeded, this is indicated to the user. To develop and optimize the toolbox, two annotators reviewed a data set of 1,200 ECG leads to assess their quality, and each lead was classified as acceptable or unacceptable. The evaluation of the toolbox was done with a new data set of 4,200 leads, which were annotated the same way. This evaluation shows that the ECGAssess toolbox correctly classified over 94% of the 4,200 ECG leads as either acceptable or unacceptable in comparison to the annotations. |
format | Online Article Text |
id | pubmed-9120362 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91203622022-05-21 ECGAssess: A Python-Based Toolbox to Assess ECG Lead Signal Quality Kramer, Linus Menon, Carlo Elgendi, Mohamed Front Digit Health Digital Health Electrocardiography (ECG) is the method most often used to diagnose cardiovascular diseases. To obtain a high-quality recording, the person conducting an ECG must be a trained expert. When these experts are not available, this important diagnostic tool cannot be used, consequently impacting the quality of healthcare. To avoid this problem, it must be possible for untrained healthcare professionals to record diagnostically useful ECGs so they can send the recordings to experts for diagnosis. The ECGAssess Python-based toolbox developed in this study provides feedback regarding whether ECG signals are of adequate quality. Each lead of the 12-lead recordings was classified as acceptable or unacceptable. This feedback allows people to identify and correct errors in the use of the ECG device. The toolbox classifies the signals according to stationary, heart rate, and signal-to-noise ratio. If the limits of these three criteria are exceeded, this is indicated to the user. To develop and optimize the toolbox, two annotators reviewed a data set of 1,200 ECG leads to assess their quality, and each lead was classified as acceptable or unacceptable. The evaluation of the toolbox was done with a new data set of 4,200 leads, which were annotated the same way. This evaluation shows that the ECGAssess toolbox correctly classified over 94% of the 4,200 ECG leads as either acceptable or unacceptable in comparison to the annotations. Frontiers Media S.A. 2022-05-06 /pmc/articles/PMC9120362/ /pubmed/35601886 http://dx.doi.org/10.3389/fdgth.2022.847555 Text en Copyright © 2022 Kramer, Menon and Elgendi. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Digital Health Kramer, Linus Menon, Carlo Elgendi, Mohamed ECGAssess: A Python-Based Toolbox to Assess ECG Lead Signal Quality |
title | ECGAssess: A Python-Based Toolbox to Assess ECG Lead Signal Quality |
title_full | ECGAssess: A Python-Based Toolbox to Assess ECG Lead Signal Quality |
title_fullStr | ECGAssess: A Python-Based Toolbox to Assess ECG Lead Signal Quality |
title_full_unstemmed | ECGAssess: A Python-Based Toolbox to Assess ECG Lead Signal Quality |
title_short | ECGAssess: A Python-Based Toolbox to Assess ECG Lead Signal Quality |
title_sort | ecgassess: a python-based toolbox to assess ecg lead signal quality |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9120362/ https://www.ncbi.nlm.nih.gov/pubmed/35601886 http://dx.doi.org/10.3389/fdgth.2022.847555 |
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