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用于无创血糖预测的传感器系统和数据分析框架的开发。

Development of sensor system and data analytic framework for non-invasive blood glucose prediction.

机构信息

Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, India.

出版信息

Sci Rep. 2024 Apr 22;14(1):9206. doi: 10.1038/s41598-024-59744-7.

Abstract

Periodic quantification of blood glucose levels is performed using painful, invasive methods. The proposed work presents the development of a noninvasive glucose-monitoring device with two sensors, i.e., finger and wrist bands. The sensor system was designed with a near-infrared (NIR) wavelength of 940 nm emitter and a 900-1700 nm detector. This study included 101 diabetic and non-diabetic volunteers. The obtained dataset was subjected to pre-processing, exploratory data analysis (EDA), data visualization, and integration methods. Ambiguities such as the effects of skin color, ambient light, and finger pressure on the sensor were overcome in the proposed 'niGLUC-2.0v'. niGLUC-2.0v was validated with performance metrics where accuracy of 99.02%, mean absolute error (MAE) of 0.15, mean square error (MSE) of 0.22 for finger, and accuracy of 99.96%, MAE of 0.06, MSE of 0.006 for wrist prototype with ridge regression (RR) were achieved. Bland-Altman analysis was performed, where 98% of the data points were within ± 1.96 standard deviation (SD), 100% were under zone A of the Clarke Error Grid (CEG), and statistical analysis showed p < 0.05 on evaluated accuracy. Thus, niGLUC-2.0v is suitable in the medical and personal care fields for continuous real-time blood glucose monitoring.

摘要

目前,血糖水平的定期量化是通过痛苦的、有创的方法来实现的。本研究提出了一种使用两个传感器(手指和手腕带)的非侵入性血糖监测设备的开发。该传感器系统的设计采用了近红外(NIR)波长为 940nm 的发射器和 900-1700nm 的探测器。本研究包括 101 名糖尿病和非糖尿病志愿者。所获得的数据集经过预处理、探索性数据分析(EDA)、数据可视化和集成方法处理。在提出的“niGLUC-2.0v”中,克服了皮肤颜色、环境光和手指压力对传感器的影响等模糊性。niGLUC-2.0v 通过性能指标进行了验证,其中手指原型的准确性为 99.02%,平均绝对误差(MAE)为 0.15,均方误差(MSE)为 0.22,手腕原型的准确性为 99.96%,MAE 为 0.06,MSE 为 0.006,使用脊回归(RR)。进行了 Bland-Altman 分析,其中 98%的数据点在±1.96 标准差(SD)内,100%的数据点在 Clarke 误差网格(CEG)的 A 区以下,统计分析表明评估准确性的 p<0.05。因此,niGLUC-2.0v 适用于医疗和个人护理领域,用于连续实时血糖监测。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/31b7/11035575/e8cfe211499b/41598_2024_59744_Fig1_HTML.jpg

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