Temporal orders and causal vector for physiological data analysis.

Area of Science:

• Physiology
• Biomedical Data Analysis
• Time Series Analysis

Background:

• Traditional physiological analyses often use global or time-series approaches.
• Local temporal analysis is crucial for understanding data within specific protocol segments.
• Existing methods may not fully capture complex physiological interdependencies.

Purpose of the Study:

• To introduce an R package for estimating temporal orders using causal vectors (CV).
• To analyze cardiorespiratory data and assess the influence of body position and breathing style on CV.
• To provide a novel method for local temporal physiological analysis.

Main Methods:

• Development of an R package for causal vector (CV) estimation.
• Application of linear modeling and time series distance for CV calculation.
• Testing on cardiorespiratory data (tidal volume, tachogram) from athletes and a control group.

Main Results:

• The causal vector (CV) analysis revealed distinct temporal relationships in cardiorespiratory data.
• Breathing rate demonstrated a greater impact on CV than breathing depth.
• The tachogram curve showed a relative precedence over tidal volume during slower breathing.

Conclusions:

• The R package offers a valuable tool for local temporal physiological analysis.
• Breathing rate is a key determinant of cardiorespiratory temporal dynamics.
• Causal vector analysis provides insights into physiological signal interactions.