期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 68, 期 6, 页码 1882-1893出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2020.3028204
关键词
Heart rate variability; Couplings; Computational modeling; Electrocardiography; Modulation; Cardiorespiratory coupling; heart rate variability; respiratory sinus arrhythmia
资金
- BOF [C24/15/036, C24/18/097]
- FWO. VLAIO [150466: OSA+]
- OO 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 Aragon - FEDER 2014-2020 Building Europe from Aragon [BSICoS T3920R]
- Ibercaja-CAI program [IT 9/19]
- EIT Health [18453]
- Italian MIUR PRIN 2017 project [PRJ-0167]
This study compared seven state-of-the-art methods for RSA quantification using simulated data and a real-life application, with some methods showing the best performance on simulated data and capturing expected trends in changes in cardiorespiratory coupling during sleep. The objective comparison of methods presented in this study can guide future analyses and the proposed simulation model is freely accessible online for further research.
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. It is freely accessible online.
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