ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2019
Dynamical disentanglement in an analysis of oscillatory systems: an application to respiratory sinus arrhythmia.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Cardiorespiratory Interaction and Autonomic Sleep Quality Improve during Sleep in Beds Made fromInternational journal of environmental research and public health · 2021Trial
- Cardiorespiratory Dynamics as a Non-Autonomous System of Coupled Oscillators with Time-Varying Frequency Modulation.Entropy (Basel, Switzerland) · 2026Article
- An ABA-ROP toggle switch orchestrates xylem differentiation and cell wall patterning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Spatial heterogeneity of cutaneous blood flow respiratory-related oscillations quantified via laser speckle contrast imaging.PloS one · 2021Article
- Time Window Determination for Inference of Time-Varying Dynamics: Application to Cardiorespiratory Interaction.Frontiers in physiology · 2020Article
- Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2019Article
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Authors and funding
4 authors.
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Abstract
We develop a technique for the multivariate data analysis of perturbed self-sustained oscillators. The approach is based on the reconstruction of the phase dynamics model from observations and on a subsequent exploration of this model. For the system, driven by several inputs, we suggest a dynamical disentanglement procedure, allowing us to reconstruct the variability of the system's output that is due to a particular observed input, or, alternatively, to reconstruct the variability which is caused by all the inputs except for the observed one. We focus on the application of the method to the vagal component of the heart rate variability caused by a respiratory influence. We develop an algorithm that extracts purely respiratory-related variability, using a respiratory trace and times of R-peaks in the electrocardiogram. The algorithm can be applied to other systems where the observed bivariate data can be represented as a point process and a slow continuous signal, e.g. for the analysis of neuronal spiking. This article is part of the theme issue 'Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences'.
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