# Temporal orders and causal vector for physiological data analysis

## TL;DR

This paper introduces a causal vector-based method for analyzing local temporal relationships in physiological data, providing insights into signal interactions and their stability across different conditions.

## Contribution

The paper presents a novel R package for estimating temporal orders using causal vectors, applicable to physiological signals and capable of assessing signal relationships and shifts.

## Key findings

- Breathing rate influences causal vector more than depth.
- Tachogram curves precede tidal volume more at slower breathing rates.
- Method effectively assesses inter-signal relationships and their stability.

## Abstract

In addition to the global parameter- and time-series-based approaches, physiological analyses should constitute a local temporal one, particularly when analyzing data within protocol segments. Hence, we introduce the R package implementing the estimation of temporal orders with a causal vector (CV). It may use linear modeling or time series distance. The algorithm was tested on cardiorespiratory data comprising tidal volume and tachogram curves, obtained from elite athletes (supine and standing, in static conditions) and a control group (different rates and depths of breathing, while supine). We checked the relation between CV and body position or breathing style. The rate of breathing had a greater impact on the CV than does the depth. The tachogram curve preceded the tidal volume relatively more when breathing was slower. Clinical relevance - The method can assess (1) relationships between two signals, having one of the two time-shifted in a given range, (2) curves of most optimal inter-signal shift, and (c) their stability across time; also for other physiological studies.