Porta Alberto, De Maria Beatrice, Bari Vlasta, Marchi Andrea, Marinou Kalliopi, Sideri Riccardo, Mora Gabriele, Dalla Vecchia Laura
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:2933-2936. doi: 10.1109/EMBC.2016.7591344.
The study evaluates the k-nearest-neighbor (KNN) strategy for the assessment of complexity of the cardiac neural control from spontaneous fluctuations of heart period (HP). Two different procedures were assessed: i) the KNN estimation of the conditional entropy (CE) proposed by Porta et al; ii) the KNN estimation of mutual information proposed by Kozachenko-Leonenko, refined by Kraskov-Stögbauer-Grassberger and here adapted for the CE estimation. The two procedures were compared over HP variability recordings obtained at rest in supine position and during head-up tilt (HUT) in amyotrophic lateral sclerosis patients and healthy subjects. We found that the indexes derived from the two procedures were significantly correlated and both methods were able to detect the effect of HUT on HP complexity within the same group and distinguish the two populations within the same experimental condition. We recommend the use of the KNN strategy to quantify the dynamical complexity of cardiac neural control in addition to more traditional approaches.
本研究评估了k近邻（KNN）策略，用于从心动周期（HP）的自发波动评估心脏神经控制的复杂性。评估了两种不同的程序：i）Porta等人提出的条件熵（CE）的KNN估计；ii）Kozachenko-Leonenko提出的互信息的KNN估计，经Kraskov-Stögbauer-Grassberger改进并在此适用于CE估计。在肌萎缩侧索硬化症患者和健康受试者仰卧休息时以及头高位倾斜（HUT）期间获得的HP变异性记录上，对这两种程序进行了比较。我们发现，从这两种程序得出的指标显著相关，并且两种方法都能够检测到HUT对同一组内HP复杂性的影响，并在相同实验条件下区分这两个人群。除了更传统的方法外，我们建议使用KNN策略来量化心脏神经控制的动态复杂性。
