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Continuous Blood Pressure Estimation From Electrocardiogram and Photoplethysmogram During Arrhythmias
OBJECTIVE: Continuous blood pressure (BP) provides valuable information for the disease management of patients with arrhythmias. The traditional intra-arterial method is too invasive for routine healthcare settings, whereas cuff-based devices are inferior in reliability and comfortable for long-term...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509183/ https://www.ncbi.nlm.nih.gov/pubmed/33013491 http://dx.doi.org/10.3389/fphys.2020.575407 |
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author | Liu, ZengDing Zhou, Bin Li, Ye Tang, Min Miao, Fen |
author_facet | Liu, ZengDing Zhou, Bin Li, Ye Tang, Min Miao, Fen |
author_sort | Liu, ZengDing |
collection | PubMed |
description | OBJECTIVE: Continuous blood pressure (BP) provides valuable information for the disease management of patients with arrhythmias. The traditional intra-arterial method is too invasive for routine healthcare settings, whereas cuff-based devices are inferior in reliability and comfortable for long-term BP monitoring during arrhythmias. The study aimed to investigate an indirect method for continuous and cuff-less BP estimation based on electrocardiogram (ECG) and photoplethysmogram (PPG) signals during arrhythmias and to test its reliability for the determination of BP using invasive BP (IBP) as reference. METHODS: Thirty-five clinically stable patients (15 with ventricular arrhythmias and 20 with supraventricular arrhythmias) who had undergone radiofrequency ablation were enrolled in this study. Their ECG, PPG, and femoral arterial IBP signals were simultaneously recorded with a multi-parameter monitoring system. Fifteen features that have the potential ability in indicating beat-to-beat BP changes during arrhythmias were extracted from the ECG and PPG signals. Four machine learning algorithms, decision tree regression (DTR), support vector machine regression (SVR), adaptive boosting regression (AdaboostR), and random forest regression (RFR), were then implemented to develop the BP models. RESULTS: The results showed that the mean value ± standard deviation of root mean square error for the estimated systolic BP (SBP), diastolic BP (DBP) with the RFR model against the reference in all patients were 5.87 ± 3.13 and 3.52 ± 1.38 mmHg, respectively, which achieved the best performance among all the models. Furthermore, the mean error ± standard deviation of error between the estimated SBP and DBP with the RFR model against the reference in all patients were −0.04 ± 6.11 and 0.11 ± 3.62 mmHg, respectively, which complied with the Association for the Advancement of Medical Instrumentation and the British Hypertension Society (Grade A) standards. CONCLUSION: The results indicated that the utilization of ECG and PPG signals has the potential to enable cuff-less and continuous BP estimation in an indirect way for patients with arrhythmias. |
format | Online Article Text |
id | pubmed-7509183 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75091832020-10-02 Continuous Blood Pressure Estimation From Electrocardiogram and Photoplethysmogram During Arrhythmias Liu, ZengDing Zhou, Bin Li, Ye Tang, Min Miao, Fen Front Physiol Physiology OBJECTIVE: Continuous blood pressure (BP) provides valuable information for the disease management of patients with arrhythmias. The traditional intra-arterial method is too invasive for routine healthcare settings, whereas cuff-based devices are inferior in reliability and comfortable for long-term BP monitoring during arrhythmias. The study aimed to investigate an indirect method for continuous and cuff-less BP estimation based on electrocardiogram (ECG) and photoplethysmogram (PPG) signals during arrhythmias and to test its reliability for the determination of BP using invasive BP (IBP) as reference. METHODS: Thirty-five clinically stable patients (15 with ventricular arrhythmias and 20 with supraventricular arrhythmias) who had undergone radiofrequency ablation were enrolled in this study. Their ECG, PPG, and femoral arterial IBP signals were simultaneously recorded with a multi-parameter monitoring system. Fifteen features that have the potential ability in indicating beat-to-beat BP changes during arrhythmias were extracted from the ECG and PPG signals. Four machine learning algorithms, decision tree regression (DTR), support vector machine regression (SVR), adaptive boosting regression (AdaboostR), and random forest regression (RFR), were then implemented to develop the BP models. RESULTS: The results showed that the mean value ± standard deviation of root mean square error for the estimated systolic BP (SBP), diastolic BP (DBP) with the RFR model against the reference in all patients were 5.87 ± 3.13 and 3.52 ± 1.38 mmHg, respectively, which achieved the best performance among all the models. Furthermore, the mean error ± standard deviation of error between the estimated SBP and DBP with the RFR model against the reference in all patients were −0.04 ± 6.11 and 0.11 ± 3.62 mmHg, respectively, which complied with the Association for the Advancement of Medical Instrumentation and the British Hypertension Society (Grade A) standards. CONCLUSION: The results indicated that the utilization of ECG and PPG signals has the potential to enable cuff-less and continuous BP estimation in an indirect way for patients with arrhythmias. Frontiers Media S.A. 2020-09-09 /pmc/articles/PMC7509183/ /pubmed/33013491 http://dx.doi.org/10.3389/fphys.2020.575407 Text en Copyright © 2020 Liu, Zhou, Li, Tang and Miao. http://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 | Physiology Liu, ZengDing Zhou, Bin Li, Ye Tang, Min Miao, Fen Continuous Blood Pressure Estimation From Electrocardiogram and Photoplethysmogram During Arrhythmias |
title | Continuous Blood Pressure Estimation From Electrocardiogram and Photoplethysmogram During Arrhythmias |
title_full | Continuous Blood Pressure Estimation From Electrocardiogram and Photoplethysmogram During Arrhythmias |
title_fullStr | Continuous Blood Pressure Estimation From Electrocardiogram and Photoplethysmogram During Arrhythmias |
title_full_unstemmed | Continuous Blood Pressure Estimation From Electrocardiogram and Photoplethysmogram During Arrhythmias |
title_short | Continuous Blood Pressure Estimation From Electrocardiogram and Photoplethysmogram During Arrhythmias |
title_sort | continuous blood pressure estimation from electrocardiogram and photoplethysmogram during arrhythmias |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7509183/ https://www.ncbi.nlm.nih.gov/pubmed/33013491 http://dx.doi.org/10.3389/fphys.2020.575407 |
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