基于递归图的复杂性度量及其在心率变异性数据中的应用
Recurrence Plot Based Measures of Complexity and its Application to Heart Rate Variability Data
摘要：
在规则、层流或混沌行为之间的转变的知识对于理解复杂系统背后的基本机制是必不可少的。虽然几种线性方法往往不足以描述这样的过程，但有几种非线性方法需要相当长的时间观察。为了克服这些困难，我们提出了措施的复杂性的基础上的垂直结构的复发图，并将其应用到逻辑地图以及心率变异性数据。对于Logistic映射，这些措施使我们不仅能够检测到混沌和周期状态之间的转换，而且还可以识别层流状态，即混沌-混沌转换。传统的递归量化分析无法检测后者的转变。将我们的新措施应用于心率变异性数据，我们能够在发生危及生命的心律失常之前检测和量化层状阶段，从而促进对此类事件的预测。本研究结果对恶性心律失常的治疗具有重要意义。
英文摘要：
The knowledge of transitions between regular, laminar or chaotic behavior is essential to understand the underlying mechanisms behind complex systems. While several linear approaches are often insufficient to describe such processes, there are several nonlinear methods which however require rather long time observations. To overcome these difficulties, we propose measures of complexity based on vertical structures in recurrence plots and apply them to the logistic map as well as to heart rate variability data. For the logistic map these measures enable us not only to detect transitions between chaotic and periodic states, but also to identify laminar states, i.e. chaos-chaos transitions. The traditional recurrence quantification analysis fails to detect the latter transitions. Applying our new measures to the heart rate variability data, we are able to detect and quantify the laminar phases before a life-threatening cardiac arrhythmia occurs thereby facilitating a prediction of such an event. Our findings could be of importance for the therapy of malignant cardiac arrhythmias.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
