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Morales Tellez JF, Moeyersons J, Testelmans D, Buyse B, Borzée P, Van Hoof C, Groenendaal W, Van Huffel S, Varon C. Technical aspects of cardiorespiratory estimation using subspace projections and cross entropy. Physiol Meas 2021; 42. [PMID: 34571494 DOI: 10.1088/1361-6579/ac2a70] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/27/2021] [Indexed: 11/12/2022]
Abstract
BACKGROUND Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. Its quantification has been suggested as a biomarker to diagnose different diseases. Two state-of-the-art methods, based on subspace projections and entropy, are used to estimate the RSA strength and are evaluated in this paper. Their computation requires the selection of a model order, and their performance is strongly related to the temporal and spectral characteristics of the cardiorespiratory signals. OBJECTIVE To evaluate the robustness of the RSA estimates to the selection of model order, delays, changes of phase and irregular heartbeats as well as to give recommendations for their interpretation on each case. APPROACH Simulations were used to evaluate the model order selection when calculating the RSA estimates explained before, as well as 3 different scenarios that can occur in signals acquired in non-controlled environments and/or from patient populations: the presence of irregular heartbeats; the occurrence of delays between heart rate variability (HRV) and respiratory signals; and the changes over time of the phase between HRV and respiratory signals. MAIN RESULTS It was found that using a single model order for all the calculations suffices to characterize the RSA estimates correctly. In addition, the RSA estimation in signals containing more than 5 irregular heartbeats in a period of 5 minutes might be misleading. Regarding the delays between HRV and respiratory signals, both estimates are robust. For the last scenario, the two approaches tolerate phase changes up to 54°, as long as this lasts less than one fifth of the recording duration. SIGNIFICANCE Guidelines are given to compute the RSA estimates in non-controlled environments and patient populations.
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Affiliation(s)
- John Fredy Morales Tellez
- ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Flanders, BELGIUM
| | - Jonathan Moeyersons
- Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Flanders, BELGIUM
| | - Dries Testelmans
- Department of Pneumology, KU Leuven University Hospitals Leuven, Leuven, BELGIUM
| | - Bertien Buyse
- Department of Respiratory Diseases, KUL UZ Gasthuisberg, Leuven, Flanders, BELGIUM
| | - Pascal Borzée
- Department of Pneumology, KU Leuven University Hospitals Leuven, Leuven, BELGIUM
| | | | | | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Flanders, BELGIUM
| | - Carolina Varon
- Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Flanders, BELGIUM
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Reali P, Piazza C, Tacchino G, Songia L, Nazzari S, Reni G, Frigerio A, Bianchi AM. Assessing stress variations in children during the strange situation procedure: comparison of three widely used respiratory sinus arrhythmia estimation methods. Physiol Meas 2021; 42. [PMID: 34325412 DOI: 10.1088/1361-6579/ac18ff] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 07/29/2021] [Indexed: 01/01/2023]
Abstract
Objective.The respiratory sinus arrhythmia (RSA) is a well-known marker of vagal activity that can be exploited to measure stress changes. RSA is usually estimated from heart rate variability (HRV). This study aims to compare the RSA obtained with three widely adopted methods showing their strengths and potential pitfalls.Approach.The three methods are tested on 69 healthy preschoolers undergoing a stressful protocol, the strange situation procedure (SSP). We compare the RSA estimated by the Porges method, the univariate autoregressive (AR) spectral analysis of the HRV signal, and the bivariate AR spectral analysis of HRV and respirogram signals. We examine RSA differences detected across the SSP episodes and correlation between the estimates provided by each method.Main results.The Porges and the bivariate AR approaches both detected significant differences (i.e. stress variations) in the RSA measured across the SSP. However, the latter method showed higher sensitivity to stress changes induced by the procedure, with the mean RSA variation between baseline and first separation from the mother (the most stressful condition) being significantly different among methods: Porges, -17.5%; univariate AR, -18.3%; bivariate AR, -23.7%. Moreover, the performances of the Porges algorithm were found strictly dependent on the applied preprocessing.Significance.Our findings confirm the bivariate AR analysis of the HRV and respiratory signals as a robust stress assessment tool that does not require any population-specific preprocessing of the signals and warn about using RSA estimates that neglect breath information in more natural experiments, such as those involving children, in which respiratory frequency changes are extremely likely.
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Affiliation(s)
- Pierluigi Reali
- Electronics Information and Bioengineering Department, Politecnico di Milano, Milano, Italy
| | - Caterina Piazza
- Bioengineering Laboratory, Scientific Institute IRCCS E. Medea, Bosisio Parini, LC, Italy
| | - Giulia Tacchino
- Electronics Information and Bioengineering Department, Politecnico di Milano, Milano, Italy
| | - Letizia Songia
- Electronics Information and Bioengineering Department, Politecnico di Milano, Milano, Italy
| | - Sarah Nazzari
- Child Psychopathology Unit, Scientific Institute IRCCS E. Medea, Bosisio Parini, LC, Italy
| | - Gianluigi Reni
- Bioengineering Laboratory, Scientific Institute IRCCS E. Medea, Bosisio Parini, LC, Italy
| | - Alessandra Frigerio
- Child Psychopathology Unit, Scientific Institute IRCCS E. Medea, Bosisio Parini, LC, Italy
| | - Anna Maria Bianchi
- Electronics Information and Bioengineering Department, Politecnico di Milano, Milano, Italy
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Armañac-Julián P, Hernando D, Lázaro J, de Haro C, Magrans R, Morales J, Moeyersons J, Sarlabous L, López-Aguilar J, Subirà C, Fernández R, Orini M, Laguna P, Varon C, Gil E, Bailón R, Blanch L. Cardiopulmonary coupling indices to assess weaning readiness from mechanical ventilation. Sci Rep 2021; 11:16014. [PMID: 34362950 PMCID: PMC8346488 DOI: 10.1038/s41598-021-95282-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 07/20/2021] [Indexed: 02/07/2023] Open
Abstract
The ideal moment to withdraw respiratory supply of patients under Mechanical Ventilation at Intensive Care Units (ICU), is not easy to be determined for clinicians. Although the Spontaneous Breathing Trial (SBT) provides a measure of the patients' readiness, there is still around 15-20% of predictive failure rate. This work is a proof of concept focused on adding new value to the prediction of the weaning outcome. Heart Rate Variability (HRV) and Cardiopulmonary Coupling (CPC) methods are evaluated as new complementary estimates to assess weaning readiness. The CPC is related to how the mechanisms regulating respiration and cardiac pumping are working simultaneously, and it is defined from HRV in combination with respiratory information. Three different techniques are used to estimate the CPC, including Time-Frequency Coherence, Dynamic Mutual Information and Orthogonal Subspace Projections. The cohort study includes 22 patients in pressure support ventilation, ready to undergo the SBT, analysed in the 24 h previous to the SBT. Of these, 13 had a successful weaning and 9 failed the SBT or needed reintubation -being both considered as failed weaning. Results illustrate that traditional variables such as heart rate, respiratory frequency, and the parameters derived from HRV do not differ in patients with successful or failed weaning. Results revealed that HRV parameters can vary considerably depending on the time at which they are measured. This fact could be attributed to circadian rhythms, having a strong influence on HRV values. On the contrary, significant statistical differences are found in the proposed CPC parameters when comparing the values of the two groups, and throughout the whole recordings. In addition, differences are greater at night, probably because patients with failed weaning might be experiencing more respiratory episodes, e.g. apneas during the night, which is directly related to a reduced respiratory sinus arrhythmia. Therefore, results suggest that the traditional measures could be used in combination with the proposed CPC biomarkers to improve weaning readiness.
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Affiliation(s)
- Pablo Armañac-Julián
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain.
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain.
| | - David Hernando
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Jesús Lázaro
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Candelaria de Haro
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain
- CIBER de Enfermedades Respiratorias (CIBER-ES), Instituto de Salud Carlos III, Madrid, Spain
| | | | - John Morales
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Jonathan Moeyersons
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
| | - Leonardo Sarlabous
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain
| | - Josefina López-Aguilar
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain
- CIBER de Enfermedades Respiratorias (CIBER-ES), Instituto de Salud Carlos III, Madrid, Spain
| | - Carles Subirà
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya, Manresa, Spain
| | - Rafael Fernández
- CIBER de Enfermedades Respiratorias (CIBER-ES), Instituto de Salud Carlos III, Madrid, Spain
- Department of Intensive Care, Fundació Althaia, Universitat Internacional de Catalunya, Manresa, Spain
| | - Michele Orini
- Institute of Cardiovascular Science, University College London, London, UK
- Barts Heart Centre, St Bartholomews Hospital, University College London, London, UK
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Carolina Varon
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium
- Circuits and Systems (CAS) group, Delft University of Technology, Delft, The Netherlands
| | - Eduardo Gil
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) group at the Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Lluís Blanch
- Critical Care Center, Hospital Universitari Parc Taulí, Institut d'Investigació Parc Taulí I3PT, Universitat Autónoma de Barcelona, Sabadell, Spain
- CIBER de Enfermedades Respiratorias (CIBER-ES), Instituto de Salud Carlos III, Madrid, Spain
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Rozo A, Morales J, Moeyersons J, Joshi R, Caiani EG, Borzée P, Buyse B, Testelmans D, Van Huffel S, Varon C. Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions. Entropy (Basel) 2021; 23:e23080939. [PMID: 34441079 PMCID: PMC8394114 DOI: 10.3390/e23080939] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Revised: 07/13/2021] [Accepted: 07/20/2021] [Indexed: 11/16/2022]
Abstract
Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.
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Affiliation(s)
- Andrea Rozo
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
- Correspondence:
| | - John Morales
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
| | - Jonathan Moeyersons
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
| | - Rohan Joshi
- Department of Patient Care and Monitoring, Philips Research, 5656 AE Eindhoven, The Netherlands;
| | - Enrico G. Caiani
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy;
| | - Pascal Borzée
- Department of Pneumology, Leuven University Centre for Sleep and Wake Disorders, UZ Leuven, 3000 Leuven, Belgium; (P.B.); (B.B.); (D.T.)
| | - Bertien Buyse
- Department of Pneumology, Leuven University Centre for Sleep and Wake Disorders, UZ Leuven, 3000 Leuven, Belgium; (P.B.); (B.B.); (D.T.)
| | - Dries Testelmans
- Department of Pneumology, Leuven University Centre for Sleep and Wake Disorders, UZ Leuven, 3000 Leuven, Belgium; (P.B.); (B.B.); (D.T.)
| | - Sabine Van Huffel
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
| | - Carolina Varon
- STADIUS, Center of Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, 3001 Leuven, Belgium; (J.M.); (J.M.); (S.V.H.); (C.V.)
- Service de Chimie-Physique E.P., Université libre de Bruxelles, B-1050 Brussels, Belgium
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Morales J, Borzée P, Testelmans D, Buyse B, Van Huffel S, Varon C. Linear and Non-linear Quantification of the Respiratory Sinus Arrhythmia Using Support Vector Machines. Front Physiol 2021; 12:623781. [PMID: 33633586 PMCID: PMC7901929 DOI: 10.3389/fphys.2021.623781] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 01/12/2021] [Indexed: 11/13/2022] Open
Abstract
Respiratory sinus arrhythmia (RSA) is a form of cardiorespiratory coupling. It is observed as changes in the heart rate in synchrony with the respiration. RSA has been hypothesized to be due to a combination of linear and nonlinear effects. The quantification of the latter, in turn, has been suggested as a biomarker to improve the assessment of several conditions and diseases. In this study, a framework to quantify RSA using support vector machines is presented. The methods are based on multivariate autoregressive models, in which the present samples of the heart rate variability are predicted as combinations of past samples of the respiration. The selection and tuning of a kernel in these models allows to solve the regression problem taking into account only the linear components, or both the linear and the nonlinear ones. The methods are tested in simulated data as well as in a dataset of polysomnographic studies taken from 110 obstructive sleep apnea patients. In the simulation, the methods were able to capture the nonlinear components when a weak cardiorespiratory coupling occurs. When the coupling increases, the nonlinear part of the coupling is not detected and the interaction is found to be of linear nature. The trends observed in the application in real data show that, in the studied dataset, the proposed methods captured a more prominent linear interaction than the nonlinear one.
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Affiliation(s)
- John Morales
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
- Leuven.AI - KU Leuven Institute for AI, KU Leuven, Leuven, Belgium
| | - Pascal Borzée
- Department of Pneumology, UZ Leuven, Leuven, Belgium
| | | | - Bertien Buyse
- Department of Pneumology, UZ Leuven, Leuven, Belgium
| | - Sabine Van Huffel
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
- Leuven.AI - KU Leuven Institute for AI, KU Leuven, Leuven, Belgium
| | - Carolina Varon
- STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
- e-Media Research Lab, Department of Electrical Engineering, KU Leuven, Leuven, Belgium
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