Scarciglia Andrea, Catrambone Vincenzo, Bonanno Claudio, Valenza Gaetano
Bioengineering and Research Centre "E. Piaggio", and Department of Information Engineering, School of Engineering, University of Pisa, 56122 Pisa, Italy.
Department of Mathematics, University of Pisa, 56127 Pisa, Italy.
Bioengineering (Basel). 2022 Feb 16;9(2):80. doi: 10.3390/bioengineering9020080.
Several methods have been proposed to estimate complexity in physiological time series observed at different time scales, with a particular focus on heart rate variability (HRV) series. In this frame, while several complexity quantifiers defined in the multiscale domain have already been investigated, the effectiveness of a multiscale Kolmogorov-Sinai (K-S) entropy has not been evaluated yet for the characterization of heartbeat dynamics.
The use of the algorithmic information content, which is estimated through an effective compression algorithm, is investigated to quantify multiscale partition-based K-S entropy on publicly available experimental HRV series gathered from young and elderly subjects undergoing a visual elicitation task (Fantasia). Moreover, publicly available HRV series gathered from healthy subjects, as well as patients with atrial fibrillation and congestive heart failure in unstructured conditions have been analyzed as well.
Elderly people are associated with a lower HRV complexity and a more predictable cardiovascular dynamics, with significantly lower partition-based K-S entropy than the young adults. Major differences between these groups occur at partitions greater than six. In case of partition cardinality greater than 5, patients with congestive heart failure show a minimal predictability, while atrial fibrillation shows a higher variability, and hence complexity, which is actually reduced by the time coarse-graining procedure.
The proposed multiscale partition-based K-S entropy is a viable tool to investigate complex cardiovascular dynamics in different physiopathological states.
已经提出了几种方法来估计在不同时间尺度上观察到的生理时间序列的复杂性，尤其关注心率变异性（HRV）序列。在此框架下，虽然已经研究了多尺度域中定义的几种复杂性量化指标，但尚未评估多尺度柯尔莫哥洛夫 - 西奈（K - S）熵在表征心跳动力学方面的有效性。
研究通过有效压缩算法估计的算法信息内容的使用，以量化基于多尺度划分的K - S熵，该熵应用于从接受视觉诱发任务（幻想曲）的年轻和老年受试者收集的公开可用实验性HRV序列。此外，还分析了从健康受试者以及在非结构化条件下患有心房颤动和充血性心力衰竭的患者收集的公开可用HRV序列。
老年人的HRV复杂性较低，心血管动力学更具可预测性，其基于划分的K - S熵明显低于年轻人。这些组之间的主要差异出现在大于六个的划分中。在划分基数大于5的情况下，充血性心力衰竭患者的可预测性最小，而心房颤动表现出更高的变异性，因此具有更高的复杂性，而这种复杂性实际上会因时间粗粒化过程而降低。
所提出的基于多尺度划分的K - S熵是研究不同生理病理状态下复杂心血管动力学的可行工具。
