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基于入耳 PPG 的血糖监测仪:概念验证研究。

An In-Ear PPG-Based Blood Glucose Monitor: A Proof-of-Concept Study.

机构信息

Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.

出版信息

Sensors (Basel). 2023 Mar 21;23(6):3319. doi: 10.3390/s23063319.

Abstract

Monitoring diabetes saves lives. To this end, we introduce a novel, unobtrusive, and readily deployable in-ear device for the continuous and non-invasive measurement of blood glucose levels (BGLs). The device is equipped with a low-cost commercially available pulse oximeter whose infrared wavelength (880 nm) is used for the acquisition of photoplethysmography (PPG). For rigor, we considered a full range of diabetic conditions (non-diabetic, pre-diabetic, type I diabetic, and type II diabetic). Recordings spanned nine different days, starting in the morning while fasting, up to a minimum of a two-hour period after eating a carbohydrate-rich breakfast. The BGLs from PPG were estimated using a suite of regression-based machine learning models, which were trained on characteristic features of PPG cycles pertaining to high and low BGLs. The analysis shows that, as desired, an average of 82% of the BGLs estimated from PPG lie in region A of the Clarke error grid (CEG) plot, with 100% of the estimated BGLs in the clinically acceptable CEG regions A and B. These results demonstrate the potential of the ear canal as a site for non-invasive blood glucose monitoring.

摘要

监测糖尿病可挽救生命。为此,我们引入了一种新颖、非侵入性且易于部署的入耳式设备,用于连续、非侵入式地测量血糖水平(BGL)。该设备配备了低成本的商用脉搏血氧仪,其红外波长(880nm)用于获取光体积描记图(PPG)。为了严谨起见,我们考虑了一系列不同的糖尿病状况(非糖尿病、糖尿病前期、I 型糖尿病和 II 型糖尿病)。记录跨越了 9 天,从早上空腹开始,直到吃完富含碳水化合物的早餐后至少两小时。通过一系列基于回归的机器学习模型,利用与高血糖和低血糖相关的 PPG 周期特征来估计 PPG 中的 BGL。分析表明,正如预期的那样,PPG 估计的 BGL 中有平均 82%位于 Clarke 误差网格(CEG)图的 A 区,100%的估计 BGL 在临床可接受的 CEG A 和 B 区。这些结果表明耳道作为非侵入性血糖监测部位的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4c35/10057625/6b49d72d48a7/sensors-23-03319-g001.jpg

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