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Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices

Heart diseases rank among the most fatal health concerns globally, with the majority being preventable through early diagnosis and effective treatment. Electrocardiogram (ECG) analysis is critical in detecting heart diseases, as it captures the heart’s electrical activities. For continuous monitorin...

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Autores principales: Félix, Ramón A., Ochoa-Brust, Alberto, Mata-López, Walter, Martínez-Peláez, Rafael, Mena, Luis J., Valdez-Velázquez, Laura L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10649215/
https://www.ncbi.nlm.nih.gov/pubmed/37960497
http://dx.doi.org/10.3390/s23218796
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author Félix, Ramón A.
Ochoa-Brust, Alberto
Mata-López, Walter
Martínez-Peláez, Rafael
Mena, Luis J.
Valdez-Velázquez, Laura L.
author_facet Félix, Ramón A.
Ochoa-Brust, Alberto
Mata-López, Walter
Martínez-Peláez, Rafael
Mena, Luis J.
Valdez-Velázquez, Laura L.
author_sort Félix, Ramón A.
collection PubMed
description Heart diseases rank among the most fatal health concerns globally, with the majority being preventable through early diagnosis and effective treatment. Electrocardiogram (ECG) analysis is critical in detecting heart diseases, as it captures the heart’s electrical activities. For continuous monitoring, wearable electrocardiographic devices must ensure user comfort over extended periods, typically 24 to 48 h. These devices demand specialized algorithms with low computational complexity to accommodate memory and power consumption constraints. One of the most crucial aspects of ECG signals is accurately detecting heartbeat intervals, specifically the R peaks. In this study, we introduce a novel algorithm designed for wearable devices, offering two primary attributes: robustness against noise and low computational complexity. Our algorithm entails fitting a least-squares parabola to the ECG signal and adaptively shaping it as it sweeps through the signal. Notably, our proposed algorithm eliminates the need for band-pass filters, which can inadvertently smooth the R peaks, making them more challenging to identify. We compared the algorithm’s performance using two extensive databases: the meta-database QT database and the BIH-MIT database. Importantly, our method does not necessitate the precise localization of the ECG signal’s isoelectric line, contributing to its low computational complexity. In the analysis of the QT database, our algorithm demonstrated a substantial advantage over the classical Pan-Tompkins algorithm and maintained competitiveness with state-of-the-art approaches. In the case of the BIH-MIT database, the performance results were more conservative; they continued to underscore the real-world utility of our algorithm in clinical contexts.
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spelling pubmed-106492152023-10-28 Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices Félix, Ramón A. Ochoa-Brust, Alberto Mata-López, Walter Martínez-Peláez, Rafael Mena, Luis J. Valdez-Velázquez, Laura L. Sensors (Basel) Article Heart diseases rank among the most fatal health concerns globally, with the majority being preventable through early diagnosis and effective treatment. Electrocardiogram (ECG) analysis is critical in detecting heart diseases, as it captures the heart’s electrical activities. For continuous monitoring, wearable electrocardiographic devices must ensure user comfort over extended periods, typically 24 to 48 h. These devices demand specialized algorithms with low computational complexity to accommodate memory and power consumption constraints. One of the most crucial aspects of ECG signals is accurately detecting heartbeat intervals, specifically the R peaks. In this study, we introduce a novel algorithm designed for wearable devices, offering two primary attributes: robustness against noise and low computational complexity. Our algorithm entails fitting a least-squares parabola to the ECG signal and adaptively shaping it as it sweeps through the signal. Notably, our proposed algorithm eliminates the need for band-pass filters, which can inadvertently smooth the R peaks, making them more challenging to identify. We compared the algorithm’s performance using two extensive databases: the meta-database QT database and the BIH-MIT database. Importantly, our method does not necessitate the precise localization of the ECG signal’s isoelectric line, contributing to its low computational complexity. In the analysis of the QT database, our algorithm demonstrated a substantial advantage over the classical Pan-Tompkins algorithm and maintained competitiveness with state-of-the-art approaches. In the case of the BIH-MIT database, the performance results were more conservative; they continued to underscore the real-world utility of our algorithm in clinical contexts. MDPI 2023-10-28 /pmc/articles/PMC10649215/ /pubmed/37960497 http://dx.doi.org/10.3390/s23218796 Text en © 2023 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
Félix, Ramón A.
Ochoa-Brust, Alberto
Mata-López, Walter
Martínez-Peláez, Rafael
Mena, Luis J.
Valdez-Velázquez, Laura L.
Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices
title Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices
title_full Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices
title_fullStr Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices
title_full_unstemmed Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices
title_short Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices
title_sort fast parabolic fitting: an r-peak detection algorithm for wearable ecg devices
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10649215/
https://www.ncbi.nlm.nih.gov/pubmed/37960497
http://dx.doi.org/10.3390/s23218796
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