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

机译：呼吸神经生理学中复杂联结描述的现代方法

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摘要

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.
机译：呼吸是由神经系统发出的信号所产生和控制的运动所产生的基本生理过程。增进我们对人类呼吸的潜在机制的理解尤其令人关注。另一个重要的实际问题是呼吸系统的诊断，预测和控制的非侵入性程序设计，该系统作为嵌入在人体复杂的生理环境及其外部环境中的子系统起作用。本文简要介绍了一些精选的现代技术，这些技术致力于探索复杂且变化的系统和时间序列数据集。这些方法基于一维熵工具（近似熵和样本熵，分别为ApEn和SampEn），可有效评估数据集中包含的信息的规律性和复杂性，以及复杂的网络理论，递归图（ RP）策略，以及联合复杂网络重现分析模式。给出了在中枢性睡眠呼吸暂停综合症患者中记录的真实生理数据的示例性结果。尽管ApEn和SampEn被证明是检测影响睡眠中呼吸模式的病理机制的灵敏方法，但基于RP策略的定性和定量研究显示出更高的效率。此外，第二种分析模式可以实现可访问数据的多维关联，这对于研究众多生理子系统之间的耦合非常重要。建议在这一领域进行进一步的工作，以绘制睡眠期间的呼吸生理方案。

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