摘要翻译：
一个10名1型糖尿病患者的数据库，佩戴了连续血糖监测设备，可以记录他们连续14天内每分钟、全天的血糖连续变化。这些记录代表了每个患者在24小时和14天内每分钟1个血糖值的时间序列，即20160个数据点。因此，在使用数值方法时，对这些时间序列进行了匿名分析。然而，由于人类日常活动所产生的随机输入，还不能区分混沌和噪声。所以，我们决定只保留这十个病人的14个晚上。然后，根据延迟坐标嵌入方法确定时间延迟和嵌入维数，使我们可以估计每个病人的关联维数和最大Lyapunov指数。这使我们证明1型糖尿病确实可能是一个混乱的现象。一旦这一结果被确定性检验所证实，我们计算了李雅普诺夫时间，并发现这一现象的可预测性极限几乎等于90分钟睡眠-梦境周期的一半。我们希望我们的结果将证明对描述和预测血糖变化是有用的。
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英文标题：
《Is type 1 diabetes a chaotic phenomenon?》
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作者：
Jean-Marc Ginoux (LIS), Heikki Ruskeep\"a\"a, Matja\v{z} Perc, Roomila
  Naeck, V\'eronique Di Costanzo, Moez Bouchouicha (LIS), Farhat Fnaiech,
  Mounir Sayadi, Takoua Hamdi
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最新提交年份：
2019
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Physics        物理学
二级分类：Chaotic Dynamics        混沌动力学
分类描述：Dynamical systems, chaos, quantum chaos, topological dynamics, cycle expansions, turbulence, propagation
动力系统，混沌，量子混沌，拓扑动力学，循环展开，湍流，传播
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英文摘要：
  A database of ten type 1 diabetes patients wearing a continuous glucose monitoring device has enabled to record their blood glucose continuous variations every minute all day long during fourteen consecutive days. These recordings represent, for each patient, a time series consisting of 1 value of glycaemia per minute during 24 hours and 14 days, i.e., 20,160 data point. Thus, while using numerical methods, these time series have been anonymously analyzed. Nevertheless, because of the stochastic inputs induced by daily activities of any human being, it has not been possible to discriminate chaos from noise. So, we have decided to keep only the 14 nights of these ten patients. Then, the determination of the time delay and embedding dimension according to the delay coordinate embedding method has allowed us to estimate for each patient the correlation dimension and the maximal Lyapunov exponent. This has led us to show that type 1 diabetes could indeed be a chaotic phenomenon. Once this result has been confirmed by the determinism test, we have computed the Lyapunov time and found that the limit of predictability of this phenomenon is nearly equal to half the 90-minutes sleep-dream cycle. We hope that our results will prove to be useful to characterize and predict blood glucose variations.
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PDF链接：
https://arxiv.org/pdf/1907.13472