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Published on: April 8, 2022
Separation of respiratory influences from the tachogram: a methodological evaluation
Devy Widjaja1, Alexander Caicedo1, Elke Vlemincx2
1Department of Electrical Engineering - STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium; Medical Information Technologies Department, iMinds, Leuven, Belgium.
Heart rate variability (HRV) analysis is improved by separating breathing influences. This study compares methods to isolate respiratory effects, revealing otherwise masked changes in heart rate variations for better autonomic nervous system (ANS) assessment.
Area of Science:
- Cardiology
- Physiology
- Biomedical Engineering
Background:
- Heart rate variability (HRV) reflects autonomic nervous system (ANS) activity.
- Breathing significantly influences HRV, independent of ANS activity.
- Accurate HRV interpretation requires accounting for respiratory influences.
Purpose of the Study:
- To evaluate and compare algorithms for separating respiratory components from HRV data.
- To assess the robustness of these decomposition methods.
- To demonstrate the utility of respiratory-informed HRV analysis in real-world applications like stress classification.
Main Methods:
- Recording respiratory activity alongside electrocardiogram (ECG) data.
- Applying and comparing various algorithms to decompose the tachogram into respiratory and non-respiratory components.
- Conducting two comparative studies to evaluate algorithm performance and robustness.
Main Results:
- Orthogonal subspace projection and ARMAX models demonstrated superior performance in tachogram decomposition.
- These methods accurately separated respiratory influences from other heart rate variations.
- The approach revealed stress-induced HRV changes masked by respiratory patterns in a real-life case.
Conclusions:
- Separating respiratory influences is crucial for accurate HRV analysis.
- Orthogonal subspace projection and ARMAX models are effective methods for this decomposition.
- This technique enhances the ability to detect subtle HRV changes, improving physiological monitoring and stress assessment.
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