Complexity analysis of short-term cardiovascular control is traditionally performed using entropy-based approaches including corrective terms or strategies to cope with the loss of reliability of conditional distributions with pattern length. This study proposes a new approach aiming at the estimation of conditional entropy (CE) from short data segments (about 250 samples) based on the k-nearest-neighbor technique. The main advantages are: (i) the control of the loss of reliability of the conditional distributions with the pattern length without introducing a priori information; (ii) the assessment of complexity indexes without fixing the pattern length to an arbitrary low value. The approach, referred to as k-nearest-neighbor conditional entropy (KNNCE), was contrasted with corrected approximate entropy (CApEn), sample entropy (SampEn) and corrected CE (CCE), being the most frequently exploited approaches for entropy-based complexity analysis of short cardiovascular series. Complexity indexes were evaluated during the selective pharmacological blockade of the vagal and/or sympathetic branches of the autonomic nervous system. We found that KNNCE was more powerful than CCE in detecting the decrease of complexity of heart period variability imposed by double autonomic blockade. In addition, KNNCE provides indexes indistinguishable from those derived from CApEn and SampEn. Since this result was obtained without using strategies to correct the CE estimate and without fixing the embedding dimension to an arbitrary low value, KNNCE is potentially more valuable than CCE, CApEn and SampEn when the number of past samples most useful to reduce the uncertainty of future behaviors is high and/or variable among conditions and/or groups.

译文

传统上,短期心血管控制的复杂性分析是使用基于熵的方法进行的,包括校正项或策略,以应对模式长度的条件分布的可靠性损失。本研究提出了一种新的方法,旨在基于k近邻技术从短数据段 (约250个样本) 中估计条件熵 (CE)。主要优点是 :( i) 在不引入先验信息的情况下控制具有模式长度的条件分布的可靠性损失; (ii) 在不将模式长度固定为任意低值的情况下评估复杂性指标。该方法被称为k最近邻条件熵 (KNNCE),与校正的近似熵 (CApEn),样本熵 (SampEn) 和校正的CE (CCE) 进行了对比,这是最常用的方法,用于基于熵的复杂性分析短心血管系列。在自主神经系统的迷走神经和/或交感神经分支的选择性药理学阻断过程中评估了复杂性指标。我们发现,KNNCE在检测双重自主神经阻滞带来的心期变异性复杂性降低方面比CCE更强大。此外,KNNCE提供的索引与从CApEn和SampEn派生的索引无法区分。由于此结果是在不使用校正CE估计值的策略以及不将嵌入维数固定为任意低值的情况下获得的,因此KNNCE可能比CCE更有价值,CApEn和SampEn当过去的样本的数量对减少未来行为的不确定性最有用是高的和/或可变的条件和/或组之间。

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