Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography

Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure.Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement.Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Nicholas Mattia Marazzi [verfasserIn] Giovanna Guidoboni [verfasserIn] Mohamed Zaid [verfasserIn] Lorenzo Sala [verfasserIn] Salman Ahmad [verfasserIn] Laurel Despins [verfasserIn] Mihail Popescu [verfasserIn] Marjorie Skubic [verfasserIn] James Keller [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	cardiovascular physiology cardiovascular monitoring ballistocardiography physiology-based modeling evolutionary algorithm (EA) personalized modeling

Übergeordnetes Werk:	In: Frontiers in Physiology - Frontiers Media S.A., 2011, 12(2022)
Übergeordnetes Werk:	volume:12 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fphys.2021.739035

Katalog-ID:	DOAJ074114735

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allfields	10.3389/fphys.2021.739035 doi (DE-627)DOAJ074114735 (DE-599)DOAJdc2f073f3eeb441fbe2328471ab4e4f8 DE-627 ger DE-627 rakwb eng QP1-981 Nicholas Mattia Marazzi verfasserin aut Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure.Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement.Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing. cardiovascular physiology cardiovascular monitoring ballistocardiography physiology-based modeling evolutionary algorithm (EA) personalized modeling Physiology Giovanna Guidoboni verfasserin aut Giovanna Guidoboni verfasserin aut Mohamed Zaid verfasserin aut Lorenzo Sala verfasserin aut Salman Ahmad verfasserin aut Laurel Despins verfasserin aut Mihail Popescu verfasserin aut Marjorie Skubic verfasserin aut James Keller verfasserin aut In Frontiers in Physiology Frontiers Media S.A., 2011 12(2022) (DE-627)631498788 (DE-600)2564217-0 1664042X nnns volume:12 year:2022 https://doi.org/10.3389/fphys.2021.739035 kostenfrei https://doaj.org/article/dc2f073f3eeb441fbe2328471ab4e4f8 kostenfrei https://www.frontiersin.org/articles/10.3389/fphys.2021.739035/full kostenfrei https://doaj.org/toc/1664-042X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022
spelling	10.3389/fphys.2021.739035 doi (DE-627)DOAJ074114735 (DE-599)DOAJdc2f073f3eeb441fbe2328471ab4e4f8 DE-627 ger DE-627 rakwb eng QP1-981 Nicholas Mattia Marazzi verfasserin aut Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure.Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement.Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing. cardiovascular physiology cardiovascular monitoring ballistocardiography physiology-based modeling evolutionary algorithm (EA) personalized modeling Physiology Giovanna Guidoboni verfasserin aut Giovanna Guidoboni verfasserin aut Mohamed Zaid verfasserin aut Lorenzo Sala verfasserin aut Salman Ahmad verfasserin aut Laurel Despins verfasserin aut Mihail Popescu verfasserin aut Marjorie Skubic verfasserin aut James Keller verfasserin aut In Frontiers in Physiology Frontiers Media S.A., 2011 12(2022) (DE-627)631498788 (DE-600)2564217-0 1664042X nnns volume:12 year:2022 https://doi.org/10.3389/fphys.2021.739035 kostenfrei https://doaj.org/article/dc2f073f3eeb441fbe2328471ab4e4f8 kostenfrei https://www.frontiersin.org/articles/10.3389/fphys.2021.739035/full kostenfrei https://doaj.org/toc/1664-042X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022
allfields_unstemmed	10.3389/fphys.2021.739035 doi (DE-627)DOAJ074114735 (DE-599)DOAJdc2f073f3eeb441fbe2328471ab4e4f8 DE-627 ger DE-627 rakwb eng QP1-981 Nicholas Mattia Marazzi verfasserin aut Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure.Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement.Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing. cardiovascular physiology cardiovascular monitoring ballistocardiography physiology-based modeling evolutionary algorithm (EA) personalized modeling Physiology Giovanna Guidoboni verfasserin aut Giovanna Guidoboni verfasserin aut Mohamed Zaid verfasserin aut Lorenzo Sala verfasserin aut Salman Ahmad verfasserin aut Laurel Despins verfasserin aut Mihail Popescu verfasserin aut Marjorie Skubic verfasserin aut James Keller verfasserin aut In Frontiers in Physiology Frontiers Media S.A., 2011 12(2022) (DE-627)631498788 (DE-600)2564217-0 1664042X nnns volume:12 year:2022 https://doi.org/10.3389/fphys.2021.739035 kostenfrei https://doaj.org/article/dc2f073f3eeb441fbe2328471ab4e4f8 kostenfrei https://www.frontiersin.org/articles/10.3389/fphys.2021.739035/full kostenfrei https://doaj.org/toc/1664-042X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022
allfieldsGer	10.3389/fphys.2021.739035 doi (DE-627)DOAJ074114735 (DE-599)DOAJdc2f073f3eeb441fbe2328471ab4e4f8 DE-627 ger DE-627 rakwb eng QP1-981 Nicholas Mattia Marazzi verfasserin aut Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure.Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement.Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing. cardiovascular physiology cardiovascular monitoring ballistocardiography physiology-based modeling evolutionary algorithm (EA) personalized modeling Physiology Giovanna Guidoboni verfasserin aut Giovanna Guidoboni verfasserin aut Mohamed Zaid verfasserin aut Lorenzo Sala verfasserin aut Salman Ahmad verfasserin aut Laurel Despins verfasserin aut Mihail Popescu verfasserin aut Marjorie Skubic verfasserin aut James Keller verfasserin aut In Frontiers in Physiology Frontiers Media S.A., 2011 12(2022) (DE-627)631498788 (DE-600)2564217-0 1664042X nnns volume:12 year:2022 https://doi.org/10.3389/fphys.2021.739035 kostenfrei https://doaj.org/article/dc2f073f3eeb441fbe2328471ab4e4f8 kostenfrei https://www.frontiersin.org/articles/10.3389/fphys.2021.739035/full kostenfrei https://doaj.org/toc/1664-042X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022
allfieldsSound	10.3389/fphys.2021.739035 doi (DE-627)DOAJ074114735 (DE-599)DOAJdc2f073f3eeb441fbe2328471ab4e4f8 DE-627 ger DE-627 rakwb eng QP1-981 Nicholas Mattia Marazzi verfasserin aut Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure.Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement.Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing. cardiovascular physiology cardiovascular monitoring ballistocardiography physiology-based modeling evolutionary algorithm (EA) personalized modeling Physiology Giovanna Guidoboni verfasserin aut Giovanna Guidoboni verfasserin aut Mohamed Zaid verfasserin aut Lorenzo Sala verfasserin aut Salman Ahmad verfasserin aut Laurel Despins verfasserin aut Mihail Popescu verfasserin aut Marjorie Skubic verfasserin aut James Keller verfasserin aut In Frontiers in Physiology Frontiers Media S.A., 2011 12(2022) (DE-627)631498788 (DE-600)2564217-0 1664042X nnns volume:12 year:2022 https://doi.org/10.3389/fphys.2021.739035 kostenfrei https://doaj.org/article/dc2f073f3eeb441fbe2328471ab4e4f8 kostenfrei https://www.frontiersin.org/articles/10.3389/fphys.2021.739035/full kostenfrei https://doaj.org/toc/1664-042X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2022
language	English
source	In Frontiers in Physiology 12(2022) volume:12 year:2022
sourceStr	In Frontiers in Physiology 12(2022) volume:12 year:2022
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	cardiovascular physiology cardiovascular monitoring ballistocardiography physiology-based modeling evolutionary algorithm (EA) personalized modeling Physiology
isfreeaccess_bool	true
container_title	Frontiers in Physiology
authorswithroles_txt_mv	Nicholas Mattia Marazzi @@aut@@ Giovanna Guidoboni @@aut@@ Mohamed Zaid @@aut@@ Lorenzo Sala @@aut@@ Salman Ahmad @@aut@@ Laurel Despins @@aut@@ Mihail Popescu @@aut@@ Marjorie Skubic @@aut@@ James Keller @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
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language_de	englisch
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topic_title	QP1-981 Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography cardiovascular physiology cardiovascular monitoring ballistocardiography physiology-based modeling evolutionary algorithm (EA) personalized modeling
topic	misc QP1-981 misc cardiovascular physiology misc cardiovascular monitoring misc ballistocardiography misc physiology-based modeling misc evolutionary algorithm (EA) misc personalized modeling misc Physiology
topic_unstemmed	misc QP1-981 misc cardiovascular physiology misc cardiovascular monitoring misc ballistocardiography misc physiology-based modeling misc evolutionary algorithm (EA) misc personalized modeling misc Physiology
topic_browse	misc QP1-981 misc cardiovascular physiology misc cardiovascular monitoring misc ballistocardiography misc physiology-based modeling misc evolutionary algorithm (EA) misc personalized modeling misc Physiology
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title	Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography
ctrlnum	(DE-627)DOAJ074114735 (DE-599)DOAJdc2f073f3eeb441fbe2328471ab4e4f8
title_full	Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography
author_sort	Nicholas Mattia Marazzi
journal	Frontiers in Physiology
journalStr	Frontiers in Physiology
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author_browse	Nicholas Mattia Marazzi Giovanna Guidoboni Mohamed Zaid Lorenzo Sala Salman Ahmad Laurel Despins Mihail Popescu Marjorie Skubic James Keller
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author-letter	Nicholas Mattia Marazzi
doi_str_mv	10.3389/fphys.2021.739035
author2-role	verfasserin
title_sort	combining physiology-based modeling and evolutionary algorithms for personalized, noninvasive cardiovascular assessment based on electrocardiography and ballistocardiography
callnumber	QP1-981
title_auth	Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography
abstract	Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure.Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement.Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing.
abstractGer	Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure.Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement.Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing.
abstract_unstemmed	Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure.Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement.Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing.
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title_short	Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography
url	https://doi.org/10.3389/fphys.2021.739035 https://doaj.org/article/dc2f073f3eeb441fbe2328471ab4e4f8 https://www.frontiersin.org/articles/10.3389/fphys.2021.739035/full https://doaj.org/toc/1664-042X
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author2	Giovanna Guidoboni Mohamed Zaid Lorenzo Sala Salman Ahmad Laurel Despins Mihail Popescu Marjorie Skubic James Keller
author2Str	Giovanna Guidoboni Mohamed Zaid Lorenzo Sala Salman Ahmad Laurel Despins Mihail Popescu Marjorie Skubic James Keller
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up_date	2024-07-03T21:24:42.782Z
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