Arsac Laurent M
Univ. Bordeaux, CNRS, Laboratoire IMS, UMR 5218 Talence, France.
Entropy (Basel). 2023 Sep 21;25(9):1364. doi: 10.3390/e25091364.
Entropy-based and fractal-based metrics derived from heart rate variability (HRV) have enriched the way cardiovascular dynamics can be described in terms of complexity. The most commonly used multifractal testing, a method using q moments to explore a range of fractal scaling in small-sized and large-sized fluctuations, is based on detrended fluctuation analysis, which examines the power-law relationship of standard deviation with the timescale in the measured signal. A more direct testing of a multifractal structure exists based on the Shannon entropy of bin (signal subparts) proportion. This work aims to reanalyze HRV during cognitive tasks to obtain new markers of HRV complexity provided by entropy-based multifractal spectra using the method proposed by Chhabra and Jensen in 1989. Inter-beat interval durations (RR) time series were obtained in 28 students comparatively in baseline (viewing a video) and during three cognitive tasks: Stroop color and word task, stop-signal, and go/no-go. The new HRV estimators were extracted from the f/α singularity spectrum of the RR magnitude increment series, established from q-weighted stable (log-log linear) power laws, namely: (i) the whole spectrum width (MF) calculated as α - α; the specific width representing large-sized fluctuations (MF) calculated as α - α; and small-sized fluctuations (MF) calculated as α - α. As the main results, cardiovascular dynamics during Stroop had a specific MF signature while MF was rather specific to go/no-go. The way these new HRV markers could represent different aspects of a complete picture of the cognitive-autonomic interplay is discussed, based on previously used entropy- and fractal-based markers, and the introduction of distribution entropy (DistEn), as a marker recently associated specifically with complexity in the cardiovascular control.
源自心率变异性（HRV）的基于熵和基于分形的指标丰富了心血管动力学复杂性的描述方式。最常用的多重分形测试是一种使用q矩来探索小尺度和大尺度波动中一系列分形标度的方法，它基于去趋势波动分析，该分析检查测量信号中标准差与时间尺度的幂律关系。基于箱（信号子部分）比例的香农熵存在对多重分形结构更直接的测试。这项工作旨在使用Chhabra和Jensen在1989年提出的方法，重新分析认知任务期间的HRV，以获得基于熵的多重分形谱提供的HRV复杂性新标记。在28名学生中，相对地在基线（观看视频）以及三项认知任务期间获得了心跳间期持续时间（RR）时间序列：斯特鲁普颜色和单词任务、停止信号任务以及执行/不执行任务。新的HRV估计量是从RR幅度增量序列的f/α奇异性谱中提取的，该谱由q加权稳定（对数-对数线性）幂律建立，即：（i）作为α - α计算的整个谱宽度（MF）；作为α - α计算的代表大尺度波动的特定宽度（MF）；以及作为α - α计算的小尺度波动（MF）。作为主要结果，斯特鲁普任务期间的心血管动力学具有特定的MF特征，而MF对于执行/不执行任务相当具有特异性。基于先前使用的基于熵和分形的标记，以及引入分布熵（DistEn）作为最近专门与心血管控制中的复杂性相关联的标记，讨论了这些新的HRV标记如何能够代表认知-自主相互作用完整图景的不同方面。
