A Novel Nonlinear Method for Cardiovascular Data Synchronization

This study introduces and evaluates a novel nonlinear technique for synchronizing electrocardiogram (ECG) and photoplethysmogram (PPG) signals by embracing the intrinsic relationship between heart rate variability (HRV) and pulse rate variability (PRV). The proposed method utilizes normalized mutual...

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Main Authors: Daniele Padovano, Arturo Martinez-Rodrigo, Norbert Marwan, Santiago Mula Munoz, Jose J. Rieta, Raul Alcaraz
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/11006692/
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author Daniele Padovano
Arturo Martinez-Rodrigo
Norbert Marwan
Santiago Mula Munoz
Jose J. Rieta
Raul Alcaraz
author_facet Daniele Padovano
Arturo Martinez-Rodrigo
Norbert Marwan
Santiago Mula Munoz
Jose J. Rieta
Raul Alcaraz
author_sort Daniele Padovano
collection DOAJ
description This study introduces and evaluates a novel nonlinear technique for synchronizing electrocardiogram (ECG) and photoplethysmogram (PPG) signals by embracing the intrinsic relationship between heart rate variability (HRV) and pulse rate variability (PRV). The proposed method utilizes normalized mutual information (NMI) derived from cross-distance matrices (CDM) of HRV and PRV, alongside the distance matrix of a reference HRV extract. We tested the methodology on two databases containing simultaneous ECG and PPG signals, including one publicly available on Physionet, and benchmarked it against various synchronization techniques, both linear (Pearson coefficient, dynamic time warping) and nonlinear (conventional mutual information, NMI with recurrence plots, cross-recurrence plot, and determinism). Results showed that the proposed method (NMI of CDM) outperformed all others, achieving synchronization rates near 50% within a 1.2-second lag threshold. This study also comprehensively examines how signal quality and recording methodology variations affect synchronization outcomes, confirming the importance of lag threshold adjustment for real accuracy assessment. However, some limitations must be kept in mind: the proposed approach is not suited for blood pressure estimation based on HRV-PRV differences, nonlinear methods generally require higher computational resources than linear ones, and further validation is needed with additional databases from real-world scenarios similar to those used in this study.
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spelling doaj-art-b42a9d6df57f4d2d9f3b7d69149a1beb2025-08-20T01:56:48ZengIEEEIEEE Access2169-35362025-01-0113893148933110.1109/ACCESS.2025.357107011006692A Novel Nonlinear Method for Cardiovascular Data SynchronizationDaniele Padovano0https://orcid.org/0000-0003-3838-1438Arturo Martinez-Rodrigo1https://orcid.org/0000-0003-2343-3186Norbert Marwan2https://orcid.org/0000-0003-1437-7039Santiago Mula Munoz3Jose J. Rieta4https://orcid.org/0000-0002-3364-6380Raul Alcaraz5https://orcid.org/0000-0002-0942-3638Research Group in Electronic, Biomedical and Telecommunications Engineering, University of Castilla–La Mancha, Cuenca, SpainResearch Group in Electronic, Biomedical and Telecommunications Engineering, University of Castilla–La Mancha, Cuenca, SpainNonlinear Dynamics Group, Institute of Physics, University of Potsdam, Potsdam, GermanyResearch Group in Electronic, Biomedical and Telecommunications Engineering, University of Castilla–La Mancha, Cuenca, SpainElectronic Engineering Department, BioMIT.org, Universitat Politecnica de Valencia, Valencia, SpainResearch Group in Electronic, Biomedical and Telecommunications Engineering, University of Castilla–La Mancha, Cuenca, SpainThis study introduces and evaluates a novel nonlinear technique for synchronizing electrocardiogram (ECG) and photoplethysmogram (PPG) signals by embracing the intrinsic relationship between heart rate variability (HRV) and pulse rate variability (PRV). The proposed method utilizes normalized mutual information (NMI) derived from cross-distance matrices (CDM) of HRV and PRV, alongside the distance matrix of a reference HRV extract. We tested the methodology on two databases containing simultaneous ECG and PPG signals, including one publicly available on Physionet, and benchmarked it against various synchronization techniques, both linear (Pearson coefficient, dynamic time warping) and nonlinear (conventional mutual information, NMI with recurrence plots, cross-recurrence plot, and determinism). Results showed that the proposed method (NMI of CDM) outperformed all others, achieving synchronization rates near 50% within a 1.2-second lag threshold. This study also comprehensively examines how signal quality and recording methodology variations affect synchronization outcomes, confirming the importance of lag threshold adjustment for real accuracy assessment. However, some limitations must be kept in mind: the proposed approach is not suited for blood pressure estimation based on HRV-PRV differences, nonlinear methods generally require higher computational resources than linear ones, and further validation is needed with additional databases from real-world scenarios similar to those used in this study.https://ieeexplore.ieee.org/document/11006692/Electrocardiographyphotoplethysmographysignal synchronizationrecurrence analysis
spellingShingle Daniele Padovano
Arturo Martinez-Rodrigo
Norbert Marwan
Santiago Mula Munoz
Jose J. Rieta
Raul Alcaraz
A Novel Nonlinear Method for Cardiovascular Data Synchronization
IEEE Access
Electrocardiography
photoplethysmography
signal synchronization
recurrence analysis
title A Novel Nonlinear Method for Cardiovascular Data Synchronization
title_full A Novel Nonlinear Method for Cardiovascular Data Synchronization
title_fullStr A Novel Nonlinear Method for Cardiovascular Data Synchronization
title_full_unstemmed A Novel Nonlinear Method for Cardiovascular Data Synchronization
title_short A Novel Nonlinear Method for Cardiovascular Data Synchronization
title_sort novel nonlinear method for cardiovascular data synchronization
topic Electrocardiography
photoplethysmography
signal synchronization
recurrence analysis
url https://ieeexplore.ieee.org/document/11006692/
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