Methods of Nonlinear Dynamics for Heart Rate Variability Analysis

Evgeniya Gospodinova
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Abstract

The heart rate variability (HRV) analysis, based on the methods of nonlinear dynamics, can provide important information for the physiological interpretation of the functioning of the cardiovascular system and assess the risk of its pathology. The article presents methods for nonlinear analysis of HRV, united in the following groups: fractal, multifractal, graphical and informational. The application of the methods of nonlinear dynamics in the study of the information characteristics of HRV in order to distinguish healthy subjects from sick ones is an important topic from the point of view of the application of the information technologies in the field of non-invasive cardiology. After determining the values of the studied parameters with the developed software and for the distinction of the two studied groups of subjects (healthy controls and patients with arrhythmia) statistical analysis was applied. The statistical analysis was performed by t-test and receiver operating characteristic (ROC) analysis. ROC curves are constructed and the area under the curves is calculated, on the basis of which the quality of the studied methods is evaluated. The results reported in this study may be useful in classifying the states of electrocardiographic signals and serve as a landmark for comparing healthy individuals to individuals with cardiovascular disease. The high information content of the used nonlinear methods for HRV analysis opens perspectives for their future use in the diagnosis and prognosis of cardiovascular diseases.
心率变异性分析的非线性动力学方法
基于非线性动力学方法的心率变异性(HRV)分析可以为心血管系统功能的生理解释和评估其病理风险提供重要信息。本文介绍了HRV的非线性分析方法,分为分形、多重分形、图形化和信息化四大类。从信息技术在无创心脏科应用的角度来看,应用非线性动力学方法研究心率变异的信息特征以区分健康人与病人是一个重要的课题。在使用开发的软件确定研究参数的值并区分两组研究对象(健康对照组和心律失常患者)后,应用统计分析。采用t检验和受试者工作特征(ROC)分析进行统计学分析。构建ROC曲线,计算曲线下面积,以此评价研究方法的质量。本研究报告的结果可能有助于对心电图信号的状态进行分类,并可作为比较健康个体与心血管疾病个体的里程碑。非线性HRV分析方法的高信息量为其在心血管疾病诊断和预后中的应用开辟了前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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