基于ECG数据的递归网络的双峰性和标度性
Bimodality and Scaling in Recurrence Networks from ECG data
摘要：
人体心脏是一个复杂系统，可以通过心电图（ECG）记录心脏的电活动来研究心脏。心电图的任何变化或异常都可能表明心脏动力学异常。在这项工作中，我们提出了一个详细的分析ECG数据的递归网络（RN）的框架。我们展示了如何从ECG数据集构建递归网络的措施，可以量化数据的复杂性和可变性。我们的研究首次表明，从心电图RN显示其独特的双峰分布的特点。我们将其与心脏系统的复杂动力学相关，具有两个空间尺度的结构。我们还表明，有相关的信息被提取的尺度与复发阈值的措施。因此，我们观察到两个标度区域的链接密度的ECG数据，这是比较标度的RN从标准的混沌和超混沌系统和噪声。虽然双峰和缩放是所有类型ECG数据中RN的共同特征，但我们发现它们中的疾病特异性变化可以量化。
英文摘要：
Human heart is a complex system that can be studied using its electrical activity recorded as Electrocardiogram (ECG). Any variations or anomalies in the ECG can indicate abnormalities in the cardiac dynamics. In this work, we present a detailed analysis of ECG data using the framework of recurrence network (RN). We show how the measures of the recurrence networks constructed from ECG data sets, can quantify the complexity and variability underlying the data. Our study shows for the first time that the RN from ECG show the unique feature of bimodality in their degree distribution. We relate this to the complex dynamics underlying the cardiac system, with structures at two spatial scales. We also show that that there is relevant information to be extracted from the scaling of measures with recurrence threshold. Thus we observe two scaling regions in the link density for ECG data which is compared with scaling in RNs from standard chaotic and hyperchaotic systems and noise. While both bimodality and scaling are common features of RNs from all types of ECG data, we find disease specific variations in them can be quantified.
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。
