A framework based on the information domain to measure coupling changes in electrophysiological signals

Area of Science:

• Physiology
• Biomedical Engineering
• Signal Processing

Background:

• Aging and gender significantly influence physiological signals like Heart Rate (HR), Blood Pressure (BP), and Respiration (RESP).
• Existing techniques struggle to fully capture the complex interactions within electrophysiological signals.

Purpose of the Study:

• To extract features from physiological signals using linear and non-linear techniques.
• To compare young and old healthy subjects to identify aging and gender-related changes in HR, BP, and RESP.
• To propose and validate a novel method for quantifying coupling changes in cardiorespiratory and cardiovascular complexity.

Main Methods:

• Employed linear and non-linear analysis on healthy datasets of young and old subjects.
• Proposed compensated transfer entropy (cTE), an information domain approach, to quantify coupling changes.
• Investigated the predictive capacity of RESP, BP, and HR recordings for changes in other parameters.

Main Results:

• Observed a substantial decrease in information flow between BP and RR intervals (time between R-peaks), and vice-versa.
• Found a significant decrease in information flow from RESP to BP and RESP to RR.
• No significant changes were detected in information flow from BP to RESP and RR to RESP.

Conclusions:

• Linear and non-linear analyses were performed on healthy young and old subjects.
• Compensated transfer entropy (cTE) was proposed to overcome limitations of existing methods in analyzing electrophysiological signals.
• The study provided insights into the interdependencies and predictive potential of RESP, BP, and HR, assessing consistency across age groups and genders.