Arzeno Natalia M, Kearney Mark T, Eckberg Dwain L, Nolan James, Poon Chi-Sang
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:5051-4. doi: 10.1109/IEMBS.2007.4353475.
Linear and nonlinear indices of heart rate variability (HRV) have been shown to predict mortality in congestive heart failure (CHF). However, most nonlinear indices describe only the fractality or complexity of HRV but not the intrinsic chaotic properties. In the present study, we performed linear (time- and frequency-domain), complexity (sample entropy), fractal (detrended fluctuation analysis) and chaos (numerical titration) analyses on the HRV of 50 CHF patients from the United Kingdom heart failure evaluation and assessment of risk trial database. Receiver operating characteristic and survival analysis yielded the chaos level to be the best predictor of mortality (followed by low/high frequency power ratio, LF/HF), such that these indices were significant in both univariate and multivariate models. These results indicate the power of heart rate chaos analysis as a potential prognostic tool for CHF.
心率变异性（HRV）的线性和非线性指标已被证明可预测充血性心力衰竭（CHF）的死亡率。然而，大多数非线性指标仅描述HRV的分形性或复杂性，而非其内在的混沌特性。在本研究中，我们对来自英国心力衰竭评估与风险评估试验数据库的50例CHF患者的HRV进行了线性（时域和频域）、复杂性（样本熵）、分形（去趋势波动分析）和混沌（数值滴定）分析。受试者工作特征和生存分析得出混沌水平是死亡率的最佳预测指标（其次是低频/高频功率比，LF/HF），这些指标在单变量和多变量模型中均具有显著性。这些结果表明心率混沌分析作为CHF潜在预后工具的作用。
