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使用新型非侵入性低血糖监测仪实时检测夜间低血糖发作

Real-time detection of nocturnal hypoglycemic episodes using a novel non-invasive hypoglycemia monitor.

作者信息

Nguyen Hung T, Ghevondian Nejhdeh, Jones Timothy W

机构信息

University of Technology, Sydney, Broadway, NSW, Australia.

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3822-5. doi: 10.1109/IEMBS.2009.5335144.

Abstract

Hypoglycemia or low blood glucose is a common and serious side effect of insulin therapy in patients with diabetes. Hypoglycemia is unpleasant and can result in unconsciousness, seizures and even death. HypoMon is a realtime non-invasive monitor that measures relevant physiological parameters continuously to provide detection of hypoglycemic episodes in Type 1 diabetes mellitus patients (T1DM). Based on heart rate and corrected QT interval of the ECG signal, we have continued to develop effective algorithms for early detection of nocturnal hypoglycemia. From a clinical study of 24 children with T1DM, associated with natural occurrence of hypoglycemic episodes at night, their heart rates increased (1.021+/-0.264 vs. 1.068+/-0.314, P<0.053) and their corrected QT intervals increased significantly (1.030+/-0.079 vs. 1.052+/-0.078, P<0.002). It is interesting to note that QT interval and heart rate are less correlated when the patients experienced hypoglycemic episodes through natural occurrence compared to when clamp studies were performed. The overall data were organized into a training set (12 patients) and a test set (another 12 patients) randomly selected. Using the optimal Bayesian neural network which was derived from the training set with the highest log evidence, the estimated blood glucose profiles produced a significant correlation (P<0.02) against measured values in the test set.

摘要

低血糖或血糖过低是糖尿病患者胰岛素治疗常见且严重的副作用。低血糖令人不适,可能导致意识丧失、癫痫发作甚至死亡。HypoMon是一种实时无创监测仪,可连续测量相关生理参数,以检测1型糖尿病患者(T1DM)的低血糖发作情况。基于心电图信号的心率和校正QT间期,我们持续开发用于早期检测夜间低血糖的有效算法。在一项针对24名T1DM儿童的临床研究中,这些儿童夜间自然发生低血糖发作,他们的心率增加(1.021±0.264对1.068±0.314,P<0.053),校正QT间期显著增加(1.030±0.079对1.052±0.078,P<0.002)。值得注意的是,与进行钳夹研究时相比,患者自然发生低血糖发作时QT间期与心率的相关性较低。总体数据被随机分为一个训练集(12名患者)和一个测试集(另外12名患者)。使用从具有最高对数证据的训练集中得出的最优贝叶斯神经网络,估计的血糖曲线与测试集中的测量值产生了显著相关性(P<0.02)。

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