Dynamical disentanglement in an analysis of oscillatory systems: an application to respiratory sinus arrhythmia

TL;DR

This paper introduces a novel method for analyzing multivariate oscillatory data, specifically disentangling respiratory influences on heart rate variability by reconstructing phase dynamics and isolating input-specific variability.

Contribution

The authors develop a phase dynamics reconstruction technique and a disentanglement algorithm to separate input-specific variability in oscillatory systems, demonstrated on heart rate and respiration data.

Findings

Successfully extracted respiratory-related variability from heart rate data.

Applicable to other systems with point process and continuous signals.

Provides a new tool for analyzing coupled oscillatory systems.

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.