Voss A, Kurths J, Kleiner H J, Witt A, Wessel N, Saparin P, Osterziel K J, Schurath R, Dietz R
MDC Max-Delbrueck-Centrum fuer Molekulare Medizin, Franz-Volhard-Klinik, Berlin, Germany.
Cardiovasc Res. 1996 Mar;31(3):419-33.
This study introduces new methods of non-linear dynamics (NLD) and compares these with traditional methods of heart rate variability (HRV) and high resolution ECG (HRECG) analysis in order to improve the reliability of high risk stratification.
Simultaneous 30 min high resolution ECG's and long-term ECG's were recorded from 26 cardiac patients after myocardial infarction (MI). They were divided into two groups depending upon the electrical risk, a low risk group (group 2, n = 10) and a high risk group (group 3, n = 16). The control group consisted of 35 healthy persons (group 1). From these electrocardiograms we extracted standard measures in time and frequency domain as well as measures from the new non-linear methods of symbolic dynamics and renormalized entropy.
Applying discriminant function techniques on HRV analysis the parameters of non-linear dynamics led to an acceptable differentiation between healthy persons and high risk patients of 96%. The time domain and frequency domain parameters were successful in less than 90%. The combination of parameters from all domains and a stepwise discriminant function separated these groups completely (100%). Use of this discriminant function classified three patients with apparently low (no) risk into the same cluster as high risk patients. The combination of the HRECG and HRV analysis showed the same individual clustering but increased the positive value of separation.
The methods of NLD describe complex rhythm fluctuations and separate structures of non-linear behavior in the heart rate time series more successfully than classical methods of time and frequency domains. This leads to an improved discrimination between a normal (healthy persons) and an abnormal (high risk patients) type of heart beat generation. Some patients with an unknown risk exhibit similar patterns to high risk patients and this suggests a hidden high risk. The methods of symbolic dynamics and renormalized entropy were particularly useful measures for classifying the dynamics of HRV.
本研究引入非线性动力学（NLD）新方法，并将其与心率变异性（HRV）和高分辨率心电图（HRECG）分析的传统方法进行比较，以提高高危分层的可靠性。
对26例心肌梗死（MI）后心脏病患者同时记录30分钟高分辨率心电图和长期心电图。根据电风险将他们分为两组，低风险组（第2组，n = 10）和高风险组（第3组，n = 16）。对照组由35名健康人组成（第1组）。从这些心电图中，我们提取了时域和频域的标准测量值以及符号动力学和重归一化熵等新非线性方法的测量值。
在HRV分析中应用判别函数技术，非线性动力学参数对健康人和高危患者的可接受区分率达96%。时域和频域参数的成功率低于90%。所有域的参数组合和逐步判别函数能将这些组完全分开（100%）。使用该判别函数将3例明显低（无）风险的患者归类到与高危患者相同的聚类中。HRECG和HRV分析的组合显示了相同的个体聚类，但增加了分离的正值。
与经典的时域和频域方法相比，NLD方法能更成功地描述复杂的节律波动并分离心率时间序列中的非线性行为结构。这导致对正常（健康人）和异常（高危患者）心跳产生类型的区分得到改善。一些风险未知的患者表现出与高危患者相似的模式，这表明存在隐藏的高风险。符号动力学和重归一化熵方法是对HRV动力学进行分类的特别有用的措施。
