Porta Alberto, Bari Vlasta, Marchi Andrea, De Maria Beatrice, Castiglioni Paolo, di Rienzo Marco, Guzzetti Stefano, Cividjian Andrei, Quintin Luc
Department of Biomedical Sciences for Health, University of Milan, Milan, Italy. IRCCS Galeazzi Orthopedic Institute, Milan, Italy.
Physiol Meas. 2015 Apr;36(4):755-65. doi: 10.1088/0967-3334/36/4/755. Epub 2015 Mar 23.
The study compares permutation-based and coarse-grained entropy approaches for the assessment of complexity of short heart period (HP) variability recordings. Shannon permutation entropy (SPE) and conditional permutation entropy (CPE) are computed as examples of permutation-based entropies, while the k-nearest neighbor conditional entropy (KNNCE) is calculated as an example of coarse-grained conditional entropy. SPE, CPE and KNNCE were applied to ad-hoc simulated autoregressive processes corrupted by increasing amounts of broad band noise and to real HP variability series recorded after complete vagal blockade obtained via administration of a high dose of atropine (AT) in nine healthy volunteers and during orthostatic challenge induced by 90° head-up tilt (T90) in 15 healthy individuals. Over the simulated series the performances of SPE and CPE degraded more rapidly with the amplitude of the superimposed broad band noise than those of KNNCE. Over real data KNNCE identified the expected decrease of the HP variability complexity both after AT and during T90. Conversely SPE and CPE detected the decrease of HP variability complexity solely during T90 as a likely result of the more favorable signal-to-noise ratio during T90 than after AT. Results derived from both simulations and real data indicated that permutation-based entropies had a larger susceptibility to broad band noise than KNNCE. We recommend caution in applying permutation-based entropies in presence of short HP variability series characterized by a low signal-to-noise ratio.
本研究比较了基于排列和粗粒度熵方法，用于评估短心动周期（HP）变异性记录的复杂性。计算香农排列熵（SPE）和条件排列熵（CPE）作为基于排列熵的示例，而计算k近邻条件熵（KNNCE）作为粗粒度条件熵的示例。将SPE、CPE和KNNCE应用于因宽带噪声量增加而损坏的临时模拟自回归过程，以及在9名健康志愿者中通过给予高剂量阿托品（AT）获得完全迷走神经阻滞后记录的真实HP变异性系列，以及在15名健康个体中由90°头高位倾斜（T90）诱发的直立位挑战期间记录的真实HP变异性系列。在模拟系列中，与KNNCE相比，SPE和CPE的性能随着叠加宽带噪声幅度的增加而更快地下降。在真实数据中，KNNCE识别出AT后和T90期间HP变异性复杂性的预期降低。相反，SPE和CPE仅在T90期间检测到HP变异性复杂性的降低，这可能是由于T90期间的信噪比优于AT后。模拟和真实数据的结果均表明，基于排列的熵比KNNCE对宽带噪声更敏感。我们建议在存在以低信噪比为特征的短HP变异性系列时，谨慎应用基于排列的熵。
