Model-Based Evaluation of Methods for Respiratory Sinus Arrhythmia Estimation

Morales J. ; Moeyersons J. ; Armanac P. (Universidad de Zaragoza) ; Orini M. ; Faes L. ; Overeem S. ; Van Gilst M. ; Van Dijk J. ; Van Huffel S. ; Bailon R. (Universidad de Zaragoza) ; Varon C.

Resumen: 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 real-life applications, 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.

Idioma: Inglés

DOI: 10.1109/TBME.2020.3028204

Año: 2021

Publicado en: IEEE Transactions on Biomedical Engineering 68, 6 (2021), 1882-1893
ISSN: 0018-9294

Factor impacto JCR: 4.756 (2021)

Categ. JCR: ENGINEERING, BIOMEDICAL rank: 37 / 98 = 0.378 (2021) - Q2 - T2

Factor impacto CITESCORE: 9.4 - Engineering (Q1)

Factor impacto SCIMAGO: 1.298 - Biomedical Engineering (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA-FSE/T39-20R-BSICoS group
Financiación: info:eu-repo/grantAgreement/EC/H2020/813120/EU/INtegrating Magnetic Resonance SPectroscopy and Multimodal Imaging for Research and Education in MEDicine/INSPiRE-MED
Financiación: info:eu-repo/grantAgreement/EC/H2020/813483/EU/INtegrating Functional Assessment measures for Neonatal Safeguard/INFANS

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Teoría Señal y Comunicac. (Dpto. Ingeniería Electrón.Com.)