Korpelainen J T, Sotaniemi K A, Mäkikallio A, Huikuri H V, Myllylä V V
Departments of Neurology, Division of Cardiology, University of Oulu Finland.
Stroke. 1999 May;30(5):1008-13. doi: 10.1161/01.str.30.5.1008.
Traditional spectral and nonspectral methods have shown that heart rate (HR) variability is reduced after stroke. Some patients with poor outcome, however, show randomlike, complex patterns of HR behavior that traditional analysis techniques are unable to quantify. Therefore, we designed the present study to evaluate the complexity and correlation properties of HR dynamics after stroke by using new analysis methods based on nonlinear dynamics and fractals ("chaos theory").
In addition to the traditional spectral components of HR variability, we measured instantaneous beat-to-beat variability and long-term continuous variability analyzed from Poincaré plots, fractal correlation properties, and approximate entropy of R-R interval dynamics from 24-hour ambulatory ECG recordings in 30 healthy control subjects, 31 hemispheric stroke patients, and 15 brain stem stroke patients (8 medullary, 7 pontine) in the acute phase of stroke and 6 months after stroke.
In the acute phase, the traditional spectral components of HR variability and the long-term continuous variability from Poincaré plots were impaired (P<0.01) in patients with hemispheric and medullary brain stem stroke, but not in patients with pontine brain stem stroke, in comparison with control subjects. At 6 months after stroke, measures of HR variability in hemispheric stroke patients were still lower (P<0.05) than those of the control subjects. Various complexity and fractal measures of HR variability were similar in patients and control subjects. The conventional frequency domain measures of HR variability as well as the Poincaré measures showed strong correlations (Pearson correlation coefficient, r=0.68 to r=0.90) with each other but only weak correlations (r=0.09 to r=0.56) with the complexity and fractal measures of HR variability.
Hemispheric and medullary brain stem infarctions seem to damage the cardiovascular autonomic regulatory system and appear as abnormalities in the magnitude of HR variability. These abnormalities can be more easily detected with the use of analysis methods of HR variability, which are based on moment statistics, than by methods based on nonlinear dynamics. Abnormal HR variability may be involved in prognostically unfavorable cardiac complications and other known manifestations of autonomic failure associated with stroke.
传统的频谱分析和非频谱分析方法表明，卒中后心率（HR）变异性降低。然而，一些预后较差的患者表现出类似随机的、复杂的心率行为模式，传统分析技术无法对其进行量化。因此，我们设计了本研究，通过使用基于非线性动力学和分形（“混沌理论”）的新分析方法，来评估卒中后心率动力学的复杂性和相关性。
除了心率变异性的传统频谱成分外，我们还测量了瞬间逐搏变异性以及从庞加莱图分析得出的长期连续变异性、分形相关性以及来自30名健康对照者、31名半球性卒中患者和15名脑干卒中患者（8名延髓性、7名脑桥性）在卒中急性期及卒中后6个月的24小时动态心电图记录中的R-R间期动力学的近似熵。
在急性期，与对照者相比，半球性和延髓性脑干卒中患者心率变异性的传统频谱成分以及庞加莱图中的长期连续变异性受损（P<0.01），但脑桥性脑干卒中患者未受损。卒中后6个月，半球性卒中患者的心率变异性测量值仍低于对照者（P<0.05）。患者和对照者心率变异性的各种复杂性和分形测量值相似。心率变异性的传统频域测量值以及庞加莱测量值之间显示出强相关性（皮尔逊相关系数，r=0.68至r=0.90），但与心率变异性的复杂性和分形测量值仅显示出弱相关性（r=0.09至r=0.56）。
半球性和延髓性脑干梗死似乎损害了心血管自主调节系统，并表现为心率变异性大小的异常。与基于非线性动力学的方法相比，使用基于矩统计的心率变异性分析方法更容易检测到这些异常。异常的心率变异性可能与预后不良的心脏并发症以及与卒中相关的自主神经功能衰竭的其他已知表现有关。
