Journal ArticleDOI
Levenberg–Marquardt-Based Non-Invasive Blood Glucose Measurement System
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TLDR
The accuracy of proposed prototype was examined by comparing non-invasively predicted data with invasively measured reference data and it was observed that all measurements lie in A and B zones of Clarke error grid and thus clinically accurate.Abstract:
The present work focuses on the development of non-invasive blood glucose measurement device to revolutionize diabetes management and reduce severe complications associated with it. A low cost, painless and non-invasive blood glucose measurement system is designed using near-infrared (NIR) LED and four photodiodes for the purpose. NIR light emitted by LED passes through the skin and is detected by photodiodes after attenuation. The detector converts the attenuated light into a voltage signal. The interference due to background noise generated by human skin is removed by taking floating or internal reference. The voltage signal obtained from the photodiodes is calibrated using Levenberg–Marquardt-based Artificial Neural Network to obtain the glucose concentration. The accuracy of proposed prototype was examined by comparing non-invasively predicted data with invasively measured reference data. It is observed that all measurements lie in A and B zones of Clarke error grid and thus clinically accurate.read more
Citations
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Journal ArticleDOI
Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability
Marjan Gusev,Lidija Poposka,Gjoko Spasevski,Magdalena Kostoska,Bojana Koteska,Monika Simjanoska,Nevena Ackovska,Aleksandar Stojmenski,Jurij F. Tasic,Janez Trontelj +9 more
TL;DR: This study analyzes noninvasive glucose measurement approaches and presents several classification dimensions according to different criteria: size, invasiveness, analyzed media, sensing properties, applied method, activation type, response delay, measurement duration, and access to results.
Journal ArticleDOI
On the estimation of sugars concentrations using Raman spectroscopy and artificial neural networks.
TL;DR: An analysis of the performance of Raman spectroscopy, combined with feed-forward neural networks (FFNN), for the estimation of concentration percentages of glucose, sucrose, and fructose in water solutions shows that both the classifier and the fitting systems performed better than a Support Vector Machine (SVM), a linear Discriminant Analysis (LDA), a Linear Regression (LR), and interval Partial Least Squares (iPLS).
Proceedings ArticleDOI
Review on Non-Invasive Blood Glucose Measurement Techniques
D. Arul Kumar,T. Jayanthy +1 more
TL;DR: This review study is to mainly focus label brief and prearranged info about various method of non-invasive continuous monitoring of blood glucose (BG) on the marketplace.
Journal ArticleDOI
On the use of fiber lasers in non-invasive blood glucose monitoring
X. Peng,Yishu Yan,He Liu +2 more
TL;DR: In this article , the causes and classification of diabetes, background knowledge of non-invasive blood glucose monitoring, the classification of blood glucose measurement methods, and the basic theory of optical methods are described.
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Intelligent estimation of blood glucose level using wristband PPG signal and physiological parameters
TL;DR: In this paper , a non-invasive blood glucose estimation system using novel Mel frequency cepstral coefficients features of wristband photoplethysmogram signal and physiological parameters is proposed.
References
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