Cuestas Eduardo
Servicio de Pediatría y Neonatología. Hospital Privado. Cátedra de Clínica Pediátrica, Argentina.
Rev Fac Cien Med Univ Nac Cordoba. 2010;67(2):81-4.
Biological systems are highly complex systems, both spatially and temporally. They are rooted in an interdependent, redundant and pleiotropic interconnected dynamic network. The properties of a system are different from those of their parts, and they depend on the integrity of the whole. The systemic properties vanish when the system breaks down, while the properties of its components are maintained. The disease can be understood as a systemic functional alteration of the human body, which present with a varying severity, stability and durability. Biological systems are characterized by measurable complex rhythms, abnormal rhythms are associated with disease and may be involved in its pathogenesis, they are been termed "dynamic disease." Physicians have long time recognized that alterations of physiological rhythms are associated with disease. Measuring absolute values of clinical parameters yields highly significant, clinically useful information, however evaluating clinical parameters the variability provides additionally useful clinical information. The aim of this review was to study one of the most recent advances in the measurement and characterization of biological variability made possible by the development of mathematical models based on chaos theory and nonlinear dynamics, as approximate entropy, has provided us with greater ability to discern meaningful distinctions between biological signals from clinically distinct groups of patients.
生物系统在空间和时间上都是高度复杂的系统。它们根植于一个相互依存、冗余且多效的相互连接的动态网络。系统的特性与其各部分的特性不同，并且取决于整体的完整性。当系统分解时，系统特性消失，而其组成部分的特性得以保留。疾病可被理解为人体的一种系统功能改变，其具有不同程度的严重性、稳定性和持续性。生物系统的特征是具有可测量的复杂节律，异常节律与疾病相关且可能参与其发病机制，它们被称为“动态疾病”。医生们早就认识到生理节律的改变与疾病有关。测量临床参数的绝对值可产生高度显著且临床上有用的信息，然而评估临床参数的变异性可提供额外有用的临床信息。本综述的目的是研究基于混沌理论和非线性动力学发展起来的数学模型（如近似熵）在生物变异性测量和表征方面的最新进展之一，其使我们有更强的能力辨别来自临床不同患者群体的生物信号之间的有意义差异。
