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首页> 外文期刊>Pulse, IEEE >Data-Driven Phenotyping : Graphical models for model-based phenotyping of sleep apnea.
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Data-Driven Phenotyping : Graphical models for model-based phenotyping of sleep apnea.

机译：数据驱动的表型：基于模型的睡眠呼吸暂停表型的图形化模型。

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摘要

Sleep apnea is a multifactorial disease with a complex underlying physiology, which includes the chemoreflex feedback loop controlling ventilation. The instability of this feedback loop is one of the key factors contributing to a number of sleep disorders, including Cheyne?Stokes respiration and obstructive sleep apnea (OSA). A major limitation of the conventional characterization of this feedback loop is the need for labor-intensive and technically challenging experiments. In recent years, a number of techniques that bring together concepts from signal processing, control theory, and machine learning have proven effective for estimating the overall loop gain of the respiratory control system (see Figure 1) and its major components, chemoreflex gain and plant gain, from noninvasive time-series measurements of ventilation and blood gases. The purpose of this article is to review the existing model-based techniques for phenotyping of sleep apnea, and some of the emerging methodologies, under a unified modeling framework known as graphical models. The hope is that the graphical model perspective provides insight into the future development of techniques for model-based phenotyping. Ultimately, such approaches have major clinical relevance since strategies to manipulate physiological parameters may improve sleep apnea severity. For example, oxygen therapy or drugs such as acetazolamide may be used to reduce chemoreflex gain, which may improve sleep apnea in selected patients.
机译：睡眠呼吸暂停是一种具有复杂基础生理的多因素疾病，其中包括控制通气的化学反射反馈回路。该反馈回路的不稳定性是导致许多睡眠障碍的关键因素之一，包括Cheyne？Stokes呼吸和阻塞性睡眠呼吸暂停（OSA）。该反馈回路的常规表征的主要局限性是需要大量劳动和技术挑战性的实验。近年来，已证明将信号处理，控制理论和机器学习等概念结合在一起的许多技术可有效地估计呼吸控制系统的总体回路增益（参见图1）及其主要组件，化学反射增益和设备。得益于通气和血气的无创时间序列测量。本文的目的是在称为图形模型的统一建模框架下，回顾现有的基于模型的睡眠呼吸暂停表型分析技术，以及一些新兴方法。希望图形模型的观点可以洞悉基于模型的表型技术的未来发展。最终，由于操纵生理参数的策略可改善睡眠呼吸暂停的严重程度，因此这些方法具有重大的临床意义。例如，可以使用氧气疗法或诸如乙酰唑胺之类的药物来减少化学反射增加，从而改善某些患者的睡眠呼吸暂停。

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