BACKGROUND:Globally, the cases of diabetes mellitus (diabetes) have increased in the past three decades, and it is recorded as one of the leading cause of death. This epidemic is a metabolic condition where the body cannot regulate blood glucose, thereby leading to abnormally high blood sugar. Genetic condition plays a significant role to determine a person susceptibility to the condition, a sedentary lifestyle and an unhealthy diet are behaviour that supports the current global epidemic. The complication that arises from diabetes includes loss of vision, peripheral neuropathy, cardiovascular complications and so on. Victims of this condition require constant monitoring of blood glucose which is done by the pricking of the finger. This procedure is painful, inconvenient and can lead to disease infection. Therefore, it is important to find a way to measure blood glucose non-invasively to minimize or eliminate the disadvantages encountered with the usual monitoring of blood glucose. METHOD:In this paper, we performed two experiments on 16 participants while electrocardiogram (ECG) data was continuously captured. In the first experiment, participants are required to consume 75 g of anhydrous glucose solution (oral glucose tolerance test) and the second experiment, no glucose solution was taken. We explored statistical and spectral analysis on HRV, HR, R-H, P-H, PRQ, QRS, QT, QTC and ST segments derived from ECG signal to investigate which segments should be considered for the possibility of achieving non-invasive blood glucose monitoring. In the statistical analysis, we examined the pattern of the data with the boxplot technique to reveal the change in the statistical properties of the data. Power spectral density estimation was adopted for the spectral analysis to show the frequency distribution of the data. RESULTS:HRV segment obtained a statistical score of 81% for decreasing pattern and HR segment have the same statistical score for increasing pattern among the participants in the first quartile, median and mean properties. While ST segment has a statistical score of 81% for decreasing pattern in the third quartile, QT segment has 81% for increasing pattern for the median. From a total change score of 6, ST, QT, PRQ, P-H, HR and HRV obtained 4, 5, 4, 5 and 6 respectively. For spectral analysis, HRV and HR segment scored 81 and 75% respectively. ST, QT, PRQ have 75, 62 and 68% respectively. CONCLUSIONS:The results obtained demonstrate that HR, HRV, PRQ, QT and ST segments under a normal, healthy condition are affected by glucose and should be considered for modelling a system to achieve the possibility of non-invasive blood glucose measurement with ECG.

译文

背景:在全球范围内,在过去的三十年中,糖尿病(糖尿病)的病例有所增加,被记录为主要的死亡原因之一。这种流行病是人体无法调节血糖从而导致异常高血糖的一种新陈代谢疾病。遗传病在确定人对疾病的易感性方面起着重要作用,久坐的生活方式和不健康的饮食是支持当前全球流行的行为。糖尿病引起的并发症包括视力丧失,周围神经病变,心血管并发症等。这种情况的受害者需要不断地监测血液中的血糖,这要通过刺破手指来完成。该过程是痛苦的,不便的并且可能导致疾病感染。因此,重要的是找到一种非侵入性地测量血糖的方法,以最小化或消除常规血糖监测中遇到的缺点。
方法:本文在连续捕获心电图(ECG)数据的同时对16名参与者进行了两个实验。在第一个实验中,要求参与者食用75微克无水葡萄糖溶液(口服葡萄糖耐量试验),而在第二个实验中,不服用葡萄糖溶液。我们探索了从心电图信号得出的HRV,HR,R-H,P-H,PRQ,QRS,QT,QTC和ST段的统计和频谱分析,以研究应考虑哪些段以实现无创血糖监测。在统计分析中,我们使用箱线图技术检查了数据的模式以揭示数据统计特性的变化。功率谱密度估计用于频谱分析,以显示数据的频率分布。
结果:在第一个四分位数,中位数和均值属性的参与者中,HRV区段的减少模式统计得分为81%,HR区段的增加模式统计得分相同。尽管ST段在第三个四分位数中减少模式的统计得分为81%,但QT段中位数在增加模式中的得分为81%。从总变化得分6中,ST,QT,PRQ,P-H,HR和HRV分别为4、5、4、5和6。对于频谱分析,HRV和HR得分分别为81和75%。 ST,QT和PRQ分别占75%,62%和68%。
结论:获得的结果表明,正常,健康条件下的HR,HRV,PRQ,QT和ST段受葡萄糖影响,应考虑为系统建模以实现使用ECG进行无创血糖测量的可能性。

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