Pincus S M
Math Biosci. 1994 Aug;122(2):161-81. doi: 10.1016/0025-5564(94)90056-6.
Numerous calculations in diverse biological settings associate greater regularity and decreased complexity of experimental time series with disease and pathology, often accompanied by claims that such calculations indicate chaotic behavior. While the claims of chaos are unresolved, it nonetheless seems important to determine a unifying theme suggesting greater signal regularity in myriad complicated physiologic systems. Our major hypothesis is that in many systems, greater regularity corresponds to greater component autonomy and isolation. The idea is that healthy systems have good lines of communication, whereas crucial biologic messages in diseased states are either slow to transmit and receive or unable to arrive. We employ ApEn, approximate entropy, to quantify regularity and confirm the hypothesis via analysis of several very different, representational mathematical model forms, conferring a robustness to model form of the hypothesis. This hypothesis is experimentally verifiable in settings where some of the crucial network nodes and connections are known.
在各种生物环境中的大量计算表明，实验时间序列的规律性增强和复杂性降低与疾病及病理状态相关，且常常伴随着这样的说法，即此类计算表明存在混沌行为。虽然关于混沌的说法尚无定论，但确定一个统一的主题，以表明无数复杂生理系统中存在更强的信号规律性，似乎仍然很重要。我们的主要假设是，在许多系统中，更强的规律性对应着更大的组件自主性和隔离性。其观点是，健康的系统具有良好的通信线路，而患病状态下关键的生物信息要么传递和接收缓慢，要么无法到达。我们采用近似熵（ApEn）来量化规律性，并通过分析几种非常不同的代表性数学模型形式来验证这一假设，从而赋予该假设对模型形式的稳健性。在一些关键网络节点和连接已知的情况下，这个假设是可以通过实验验证的。
