IEEE Trans Biomed Eng. 2019 Jan;66(1):80-88. doi: 10.1109/TBME.2018.2825500. Epub 2018 Apr 12.
Numerous indices were devised for the statistical characterization of temporal dynamics of heart rate variability (HRV) with the aim to discriminate between healthy subjects and nonhealthy patients. Elaborating on the concepts of (multi)fractal and nonlinear analyses, the present contribution defines and studies formally novel non Gaussian multiscale representations.
A methodological framework for non Gaussian multiscale representations constructed on wavelet p-leaders is developed, relying a priori neither on exact scale-free dynamics nor on predefined forms of departure from Gaussianity. Its versatility in quantifying the strength and nature of departure from Gaussian is analyzed theoretically and numerically. The ability of the representations to discriminate between healthy subjects and congestive heart failure (CHF) patients, and between survivors and nonsurvivor CHF patients, is assessed on a large cohort of 198 subjects.
The analysis leads to conclude that i) scale-free and multifractal dynamics are observed, both for healthy subjects and CHF patients, for time scales shorter than [Formula: see text]; ii) a circadian evolution of multifractal and non Gaussian properties of HRV is evidenced for healthy subjects, but not for CHF patients; iii) non Gaussian multiscale indices possess high discriminative abilities between survivor and nonsurvivor CHF patients, at specific time scales ([Formula: see text] and [Formula: see text]).
The non Gaussian multiscale representations provide evidence for the existence of short-term cascade-type multifractal mechanisms underlying HRV for both healthy and CHF subjects. A circadian evolution of this mechanism is only evidenced for the healthy group, suggesting an alteration of the sympathetic-parasympathetic balance for CHF patients.
Results obtained for a large cohort of subjects suggest that the novel non Gaussian indices might robustly quantify crucial information for clinical risk stratification in CHF patients.
为了区分健康受试者和非健康患者，已经设计了许多指数来对心率变异性（HRV）的时间动态进行统计学描述。基于（多）分形和非线性分析的概念，本研究正式定义并研究了新的非高斯多尺度表示。
提出了一种基于小波 p-领导者的非高斯多尺度表示的方法框架，该框架既不依赖于精确的无标度动力学，也不依赖于预先设定的偏离高斯的形式。从理论和数值上分析了其量化偏离高斯程度的强度和性质的多功能性。在 198 名受试者的大样本中，评估了这些表示在区分健康受试者和充血性心力衰竭（CHF）患者以及区分 CHF 幸存者和非幸存者方面的能力。
分析结果表明：i）无论是健康受试者还是 CHF 患者，在短于[Formula: see text]的时间尺度上，都观察到了无标度和多重分形动力学；ii）健康受试者的 HRV 多重分形和非高斯特性存在昼夜演化，而 CHF 患者则没有；iii）在特定的时间尺度[Formula: see text]和[Formula: see text]上，非高斯多尺度指数具有区分 CHF 幸存者和非幸存者的高判别能力。
非高斯多尺度表示为 HRV 存在短期级联型多重分形机制提供了证据，无论是健康受试者还是 CHF 患者都是如此。这种机制的昼夜演化仅在健康组中得到证实，表明 CHF 患者的交感神经-副交感神经平衡发生了改变。
对大样本受试者的研究结果表明，新的非高斯指数可能能够稳健地量化 CHF 患者临床风险分层的关键信息。
