Detection of cardiorespiratory interaction for clinical research applications.

Abstract

Human physiological systems are a widely studied topic in the field of Biomedical Engineering. There is a particular interest in the study of human cardiovascular and respiratory systems since these two systems do not act independently; there exists a strong coupling between them. Experimental studies use the concept of synchronization to demonstrate the interaction between different physiological systems. Synchronization is the appearance of some relationship between two periodic oscillators in the form of locking of their phases or adjustment of rhythms. Cardiorespiratory coordination is an aspect of the interaction between heart and respiratory rhythm that has been reported not only at rest or during exercise, but also in subjects under the influence of anesthesia and drugs. Through the quantification of cardiorespiratory coordination we can achieve a better understanding of its physiological functioning. Some of the conventional signal-processing techniques such as power spectral density and cross-correlation analysis have shown linear dependencies between heart and respiratory rate. However, as these biological signals are inherently non-linear, nonstationary, and contain superimposed noise, the techniques mentioned above often prove to be inadequate for characterizing their complex dynamics. Therefore, to overcome these issues, it is required to develop a technique that is less sensitive to noise, robust and possibly provides additional information about the interaction between cardiac rhythms and respiration. This Thesis introduces a new and relatively simple approach for the quantification of cardiorespiratory interaction based on joint symbolic dynamics, which provides an easy interpretation of physiological data by a simplified description of the system’s dynamics. Furthermore, this Thesis investigates the association between cardiorespiratory coordination and some of the physiological mechanisms, and assesses cardiorespiratory coordination as a marker of cardiorespiratory system disturbances.