Causality in cardiorespiratory signals in pediatric cardiac patients

TL;DR

This study evaluates four Granger causality methods, including linear and nonlinear approaches, to assess and quantify respiratory sinus arrhythmia in pediatric cardiac patients using ECG and impedance signals.

Contribution

It compares multiple causality detection methods on pediatric cardiac data, highlighting their ability to infer causal relationships in physiological signals.

Findings

All methods detected causal dependency between signals.

Correlations between RSA and demographics varied by method.

Nonlinear methods provided additional insights.

Abstract

Four different Granger causality-based methods - one linear and three nonlinear (Granger Causality, Kernel Granger Causality, large-scale Nonlinear Granger Causality, and Neural Network Granger Causality) were used for assessment and causal-based quantification of the respiratory sinus arrythmia (RSA) in the group of pediatric cardiac patients, based on the single-lead ECG and impedance pneumography signals (the latter as the tidal volume curve equivalent). Each method was able to detect the dependency (in terms of causal inference) between respiratory and cardiac signals. The correlations between quantified RSA and the demographic parameters were also studied, but the results differ for each method.