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Hypoglycaemia-Related EEG Changes Assessed by Approximate Entropy

机译：与近似熵评估的低血糖相关的EEG变化

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摘要

Several studies performed in human beings demonstrated that glucose concentration in blood can affect EEG rhythms, typically evaluated by standard spectral analysis techniques. In the present work, we investigate if EEG complexity assessed by a nonlinear algorithm, Approximate Entropy (ApEn), reflects changes of glucose concentration levels during an induced hypoglycaemia experiment. In particular, in 10 type-1 diabetic volunteers, ApEn was computed from the P3-C3 EEG channel at different temporal scales and then correlated to the three classes of glycaemic states, i.e. hyper/eu/hypo-glycaemia. Results show that, for all considered temporal scales, EEG complexity in hypoglycaemia is lower, with statistical significance, than in eu- and in hyper-glycaemia. No statistically significant difference can be evidenced between ApEn values in hyper- and in eu-glycaemic states. In conclusion, in addition to power indexes in the four traditional EEG bands, other indicators, and ApEn in particular, can be used to quantitatively investigate glucose-related EEG changes.
机译：在人类中进行的几项研究表明，血液中的葡萄糖浓度可以影响EEG节律，通常通过标准光谱分析技术评估。在本作本作中，我们研究了非线性算法评估的EEG复杂性，近似熵（APEN），反映了诱导的低血糖实验期间葡萄糖浓度水平的变化。特别地，在10型糖尿病患者中，从不同的时间尺度的P3-C3脑电图频道计算APEN，然后与三类血糖状态相关，即HEAD / EU / HYPO-糖尿病。结果表明，对于所有被认为的时间尺度，低血糖中的EEG复杂性较低，统计显着性比在欧盟和高血糖血症中。在超级和欧盟血糖状态的APEN值之间没有统计学显着差异。总之，除了四个传统EEG带中的功率指标​​之外，还可以使用其他指标和APEN，以定量研究葡萄糖相关的EEG变化。

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