Removing subject dependencies on Non-Invasive Blood Glucose Measurement using Hybrid Techniques

Pathirage, Kasun; Roopasinghe, Pubudu; Sooriyaarachchi, Jinani J.; Weththasinghe, Rasangika; Nanayakkara, Nuwan D.

doi:10.1109/embc.2019.8856391

Cited by 5 publications

(10 citation statements)

References 12 publications

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“…In [17], the measurement frequency of bioelectrical impedance ranged from 50 to 100 kHz, with an interval of 5 kHz. The present study used bioelectrical impedance data measured by currents of 11 frequencies, with the bioelectrical impedance data of each frequency involving a real part, imaginary part, phase, and amplitude values.…”

Section: Physiological Parameter Feature Extractionmentioning

confidence: 99%

“…The present study used bioelectrical impedance data measured by currents of 11 frequencies, with the bioelectrical impedance data of each frequency involving a real part, imaginary part, phase, and amplitude values. This study referenced [17,[19][20][21][22] and used the patient characteristics of age, height, weight, heart rate, blood flow velocity, hemoglobin, and blood oxygen saturation as the physiological parameters for blood glucose estimation.…”

Section: Physiological Parameter Feature Extractionmentioning

confidence: 99%

“…Pathirage et al obtained a multiwavelength near-infrared light spectrum from multi-wavelength infrared light and extracted the features of bioelectrical impedance every 0.5 kHz from 50 to 100 kHz. Furthermore, they used a commercially available glucose meter to obtain the blood glucose of research participants, which was used to train a random forest regression model, obtaining an accuracy rate of 90.7% [17].…”

Section: Introductionmentioning

confidence: 99%

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Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring

Yen

Chen

Wang

et al. 2022

Sensors

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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 (R Squared, R2) of 0.997, all of which fall within the clinically accurate region A in the Clarke error grid analyses.

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Section: Physiological Parameter Feature Extractionmentioning

confidence: 99%

Section: Physiological Parameter Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring

Yen

Chen

Wang

et al. 2022

Sensors

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“…There is room to improve the performance of non-invasive glucose sensors for public use under regular home conditions. These non-invasive glucose measurement techniques are based on infrared spectroscopy [6]- [8], Raman spectroscopy [9], [10], polarimetry [11], photoacoustic spectroscopy [12], millimeter wave/microwave spectroscopy [13]- [15], bio-impedance spectroscopy [16], optical coherence tomography [17], and hybrid techniques [18], [19].…”

Section: Introductionmentioning

confidence: 99%

“…The complexity of non-invasive glucose measurements necessitates the use of more effective algorithms capable of dealing with higher dimensional data in order to improve the accuracy of non-invasive glucose concentration measurements. Various machine learning techniques are used for glucose predictions, such as multiple linear regression [21], partial least square regression [22], [23], principal component regression [22], feed forward neural networks [24], [25], deep neural networks [26], [27], support vector machines [27], [28], random forest regression [18], etc. These techniques are used to build regression based models that predict continuous values of glucose concentration.…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications

et al. 2021

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Diabetes is a major public health challenge affecting more than 451 million people. Physiological and experimental factors influence the accuracy of non-invasive glucose monitoring, and these need to be overcome before replacing the finger prick method. Also, the suitable employment of machine learning techniques can significantly improve the accuracy of glucose predictions. One aim of this study is to use light sources with multiple wavelengths to enhance the sensitivity and selectivity of glucose detection in an aqueous solution. Multiple wavelength measurements have the potential to compensate for errors associated with inter-and intra-individual differences in blood and tissue components. In this study, the transmission measurements of a custom built optical sensor are examined using 18 different wavelengths between 410 and 940 nm. Results show a high correlation value (0.98) between glucose concentration and transmission intensity for four wavelengths (485, 645, 860 and 940 nm). Five machine learning methods are investigated for glucose predictions. When regression methods are used, 9% of glucose predictions fall outside the correct range (normal, hypoglycemic or hyperglycemic). The prediction accuracy is improved by applying classification methods on sets of data arranged into 21 classes. Data within each class corresponds to a discrete 10 mg/dL glucose range. Classification based models outperform regression, and among them, the support vector machine is the most successful with F1-score of 99%. Additionally, Clarke error grid shows that 99.75% of glucose readings fall within the clinically acceptable zones. This is an important step towards critical diagnosis during an emergency patient situation.

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Wearable Bioimpedance Measuring Devices

Bertemes-Filho

Morcelles

2021

Lecture Notes in Bioengineering

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Removing subject dependencies on Non-Invasive Blood Glucose Measurement using Hybrid Techniques

Cited by 5 publications

References 12 publications

Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring

Non-Invasive Blood Glucose Estimation System Based on a Neural Network with Dual-Wavelength Photoplethysmography and Bioelectrical Impedance Measuring

Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications

Wearable Bioimpedance Measuring Devices

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