ArticleSensors (Basel, Switzerland)2022
Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Interpretable non-invasive glucose monitoring: an attention-based deep learning framework for visualizing hemodynamic correlates in PPG signals.Frontiers in bioengineering and biotechnology · 2026Article
- Non invasive blood glucose estimation using green light photoplethysmography and machine learning.Frontiers in digital health · 2026Article
- Noninvasive subterahertz glucose monitoring using a communication inspired eye diagram.Scientific reports · 2025Article
- A Feasibility Study on Noninvasive Blood Glucose Estimation Using Machine Learning Analysis of Near-Infrared Spectroscopy Data.Biosensors · 2025Article
- Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals.Biosensors · 2025Article
- A Deep Sparse Capsule Network for Non-Invasive Blood Glucose Level Estimation Using a PPG Sensor.Sensors (Basel, Switzerland) · 2025Article
- Photoplethysmography based non-invasive blood glucose estimation using systolic-diastolic framing MFCC features and machine learning regression.BioImpacts : BI · 2025Article
- Non-Invasive Glucose Sensing Technologies and Products: A Comprehensive Review for Researchers and Clinicians.Sensors (Basel, Switzerland) · 2023Review
- [A method for photoplethysmography signal quality assessment fusing multi-class features with multi-scale series information].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Accuracy of a Non-Invasive Home Glucose Monitor for Measurement of Blood Glucose.Indian journal of endocrinology and metabolismArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This study proposed a noninvasive blood glucose estimation system based on dual-wavelength photoplethysmography (PPG) and bioelectrical impedance measuring technology that can avoid the discomfort created by conventional invasive blood glucose measurement methods while accurately estimating blood glucose. The measured PPG signals are converted into mean, variance, skewness, kurtosis, standard deviation, and information entropy. The data obtained by bioelectrical impedance measuring consist of the real part, imaginary part, phase, and amplitude size of 11 types of frequencies, which are converted into features through principal component analyses. After combining the input of seven physiological features, the blood glucose value is finally obtained as the input of the back-propagation neural network (BPNN). To confirm the robustness of the system operation, this study collected data from 40 volunteers and established a database. From the experimental results, the system has a mean squared error of 40.736, a root mean squared error of 6.3824, a mean absolute error of 5.0896, a mean absolute relative difference of 4.4321%, and a coefficient of determination (
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Registered trials
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