计算机科学
级联
均方误差
近似误差
卷积神经网络
卷积(计算机科学)
天线(收音机)
信号(编程语言)
模式识别(心理学)
对偶(语法数字)
人工神经网络
人工智能
算法
统计
电信
数学
艺术
化学
文学类
色谱法
程序设计语言
作者
Zengxiang Wang,Xia Xiao,Yanwei Pang,Wenling Su
标识
DOI:10.1088/1361-6501/acd138
摘要
Abstract Finger-prick blood collection process has become unrealistic for a long-term and frequent blood glucose detection. Hence, an appropriate non-invasive detection system is highly desirable to effectively address this concern. A non-invasive and intelligent dual-sensing system is forwarded in this paper. The feasibility of the proposed system has been verified using glucose solution, animal serum, and human trials. In the in vivo experiments, the detection signal exhibited a high correlation ( r = 0.96) with blood glucose levels. An improved cascade convolution neural network is suggested to accurately predict the BGL. For the estimation results of BGL, the root mean squared error of 7.3217 mg dl −1 and a mean absolute relative difference of 4.7209% are achieved. The estimated results also fell by 100% in the clinically acceptable zones of the Clarke error grid analysis, indicating that the proposed system could potentially be used for clinical measurements.
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