Cuesta-Frau David, Novák Daniel, Burda Vacláv, Molina-Picó Antonio, Vargas Borja, Mraz Milos, Kavalkova Petra, Benes Marek, Haluzik Martin
Technological Institute of Informatics, Universitat Politècnica de València, Alcoi Campus, 03801 Alcoi, Spain.
Department of Cybernetics, Czech Technical University in Prague, 16000 Prague, Czech Republic.
Entropy (Basel). 2018 Nov 12;20(11):871. doi: 10.3390/e20110871.
This paper analyses the performance of SampEn and one of its derivatives, Fuzzy Entropy (FuzzyEn), in the context of artifacted blood glucose time series classification. This is a difficult and practically unexplored framework, where the availability of more sensitive and reliable measures could be of great clinical impact. Although the advent of new blood glucose monitoring technologies may reduce the incidence of the problems stated above, incorrect device or sensor manipulation, patient adherence, sensor detachment, time constraints, adoption barriers or affordability can still result in relatively short and artifacted records, as the ones analyzed in this paper or in other similar works. This study is aimed at characterizing the changes induced by such artifacts, enabling the arrangement of countermeasures in advance when possible. Despite the presence of these disturbances, results demonstrate that SampEn and FuzzyEn are sufficiently robust to achieve a significant classification performance, using records obtained from patients with duodenal-jejunal exclusion. The classification results, in terms of area under the ROC of up to 0.9, with several tests yielding AUC values also greater than 0.8, and in terms of a leave-one-out average classification accuracy of 80%, confirm the potential of these measures in this context despite the presence of artifacts, with SampEn having slightly better performance than FuzzyEn.
本文分析了样本熵（SampEn）及其一种衍生方法模糊熵（FuzzyEn）在含伪差的血糖时间序列分类中的性能。这是一个困难且实际上尚未探索的框架，在此框架下，更敏感和可靠的测量方法的可用性可能具有重大临床意义。尽管新的血糖监测技术的出现可能会降低上述问题的发生率，但不正确的设备或传感器操作、患者依从性、传感器脱落、时间限制、采用障碍或可承受性等仍可能导致相对较短且含有伪差的记录，就像本文或其他类似研究中所分析的记录一样。本研究旨在表征此类伪差所引起的变化，以便在可能的情况下提前安排应对措施。尽管存在这些干扰，但结果表明，使用从十二指肠 - 空肠转位患者获得的记录，样本熵和模糊熵具有足够的鲁棒性，能够实现显著的分类性能。分类结果显示，受试者工作特征曲线（ROC）下面积高达0.9，多项测试的AUC值也大于0.8，留一法平均分类准确率为80%，这证实了在存在伪差的情况下这些测量方法在此背景下的潜力，样本熵的性能略优于模糊熵。
