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Technical aspects of cardiorespiratory estimation using subspace projections and cross entropy
John Morales1,2, Jonathan Moeyersons1,2, Dries Testelmans3
1ESAT-STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, B-3001 Leuven, Belgium.
Physiological Measurement
|September 27, 2021
Summary
This study evaluates two methods for quantifying respiratory sinus arrhythmia (RSA), a cardiorespiratory coupling biomarker. Results show consistent RSA estimation with a single model order, but irregular heartbeats can be misleading.
Area of Science:
- Cardiology
- Physiology
- Biomedical Engineering
Background:
- Respiratory sinus arrhythmia (RSA) is a cardiorespiratory coupling measure.
- RSA quantification is a potential biomarker for disease diagnosis.
- Current estimation methods rely on subspace projections and entropy.
Purpose of the Study:
- Evaluate RSA estimation robustness to model order selection.
- Assess performance under non-ideal signal conditions (delays, phase changes, irregular heartbeats).
- Provide guidelines for interpreting RSA in real-world and patient data.
Main Methods:
- Simulations used to test RSA estimation methods.
- Evaluated model order selection impact.
- Simulated scenarios: irregular heartbeats, HRV-respiratory delays, and phase variations.
Main Results:
- A single model order is sufficient for accurate RSA characterization.
- More than 5 irregular heartbeats per 5 min can yield misleading RSA estimates.
- RSA estimates are robust to delays and tolerate phase changes up to 54° if brief.
Conclusions:
- Established guidelines for computing RSA in non-controlled and patient populations.
- Recommendations for interpreting RSA estimates considering signal artifacts.
- Enhanced understanding of RSA estimation limitations and strengths.
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