Model-Based Evaluation of Methods for Respiratory Sinus Arrhythmia Estimation

John Fredy MoralesTellez (Corresponding author), Jonathan Moeyersons, Pablo Armanac, Michele Orini, Luca Faes, Sebastiaan Overeem, Merel van Gilst, Johannes van Dijk, Sabine van Huffel, Raquel Bailon, Carolina Varon

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

11 Citaten (Scopus)

Samenvatting

OBJECTIVE: Respiratory sinus arrhythmia (RSA) refers to heart rate oscillations synchronous with respiration, and it is one of the major representations of cardiorespiratory coupling. Its strength has been suggested as a biomarker to monitor different conditions and diseases. Some approaches have been proposed to quantify the RSA, but it is unclear which one performs best in specific scenarios. The main objective of this study is to compare seven state-of-the-art methods for RSA quantification using data generated with a model proposed to simulate and control the RSA. These methods are also compared and evaluated on a real-life application, for their ability to capture changes in cardiorespiratory coupling during sleep.

METHODS: A simulation model is used to create a dataset of heart rate variability and respiratory signals with controlled RSA, which is used to compare the RSA estimation approaches. To compare the methods objectively in a real-life application, regression models trained on the simulated data are used to map the estimates to the same measurement scale.

RESULTS AND CONCLUSION: RSA estimates based on cross entropy, time-frequency coherence and subspace projections showed the best performance on simulated data. In addition, these estimates captured the expected trends in the changes in cardiorespiratory coupling during sleep similarly.

SIGNIFICANCE: An objective comparison of methods for RSA quantification is presented to guide future analyses. Also, the proposed simulation model can be used to compare existing and newly proposed RSA estimates. It is freely accessible online.

Originele taal-2Engels
Artikelnummer9210854
Pagina's (van-tot)1882-1893
Aantal pagina's12
TijdschriftIEEE Transactions on Biomedical Engineering
Volume68
Nummer van het tijdschrift6
Vroegere onlinedatum1 okt. 2020
DOI's
StatusGepubliceerd - jun. 2021

Financiering

Manuscript received May 22, 2020; revised September 4, 2020; accepted September 23, 2020. Date of publication October 1, 2020; date of current version May 20, 2021. Funding: BOF, C24/15/036, C24/18/097. FWO. VLAIO, 150466: OSA+, O&O HBC 2016 0184 eWatch. IMEC funds 2020. EU H2020 MSCA-ITN-2018: INSPiRE-MED #813120. EU H2020 MSCA-ITN-2018: INFANS #813483. EIT Health - SeizeIT2 CIBER, Gobierno de Aragón (Reference Group BSICoS T39-20R) cofunded by FEDER 2014-2020 “Building Europe from Aragón”; and through a personal PhD grant to P.A; and Ibercaja-CAI program for research stays, code IT 9/19. HealthBed study was supported by a grant from EIT Health (project no. 18453), L.F. is supported by the Italian MIUR PRIN 2017 project, PRJ-0167, “Stochastic forecasting in complex systems” (Corresponding author: John Morales.) John Morales is with the ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing, and Data Analytics, KU Leuven, 3001 Leuven, Belgium and also with the KU Leuven Institute for AI, 3001 Leuven, Belgium (e-mail: jmorales@esat.kuleuven.be). Funding: BOF, C24/15/036, C24/18/097. FWO. VLAIO, 150466: OSA+, OandO HBC 2016 0184 eWatch. IMEC funds 2020. EU H2020 MSCA-ITN-2018: INSPiRE-MED #813120. EU H2020 MSCA-ITN-2018: INFANS #813483. EIT Health - SeizeIT2 CIBER, Gobierno de Aragón (Reference Group BSICoS T39-20R) cofunded by FEDER 2014-2020 “Building Europe from Aragón”; and through a personal PhD grant to P.A; and Ibercaja-CAI program for research stays, code IT 9/19. HealthBed study was supported by a grant from EIT Health (project no. 18453), L.F. is supported by the Italian MIUR PRIN 2017 project, PRJ-0167, “Stochastic forecasting in complex systems”

FinanciersFinanciernummer
Horizon 2020 Framework Programme813483, MSCA-ITN-2018
EIT Health18453
Ministero dell’Istruzione, dell’Università e della RicercaPRJ-0167
European Regional Development Fund

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