Modern Methods for the Description of Complex Couplings in the Neurophysiology of Respiration

Breathing is a fundamental physiological process produced by movements generated and controlled by efferent signals from the nervous system. Improving our understanding of the mechanisms underlying breathing in humans is of particular interest. Another important practical issue is the design of noninvasive procedures for the diagnosis, prediction, and control of the respiratory system, which works as a subsystem embedded in the complex physiological environment of the human organism and its external environment. This paper provides a concise review of a selected set of modern techniques dedicated to the exploration of complex and varying systems and sets of time-series data. These methods are based on an 1D entropic tool (approximate and sample entropy, i.e., ApEn and SampEn, respectively), which is effective for assessing the regularity and complexity of information contained in data sets, as well as complex network theory, recurrence plot (RP) strategy, and the joint complex network-recurrence analysis mode. Exemplary results are given for real physiological data recorded in patients with symptoms of central sleep apnea syndrome. Although ApEn and SampEn are shown to be sensitive methods for the detection of pathological mechanisms affecting breathing patterns during sleep, qualitative and quantitative studies based on the RP strategy reveal even better efficiency for this task. In addition, the second mode of analysis enables multi-dimensional correlation of accessible data, which is important for studying the couplings between numerous physiological subsystems. Further work in this area is proposed to map the scheme of breathing physiology during sleep.