Non-invasive blood glucose in vivo detection based on photoacoustic spectroscopy

Peng-fei, 吕鹏飞 L; Zhi-qian, 陆志谦 Lu; Qiao-zhi, 何巧芝 He; Qian, 王 倩 Wang; Hui, 赵 辉 Zhao

doi:10.3788/ope.20192706.1301

Cited by 2 publications

(1 citation statement)

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“…Wadamori [12] used a measurable depth of the blood glucose level based onphotoacoustic spectroscopy to improve detection sensitivity, and the relationship between modulation frequency and the thickness of a sample was evaluated. Lv [13] used a new method based on temperature compensation for differential photoacoustic measurement to suppress the influence of temperature in the non-invasive blood glucose in vivo photoacoustic detection. Zhang [14] used a new concept named time-domain photoacoustic waveform spectroscopy to perform the chemical component quantification of glucose.…”

Section: Introductionmentioning

confidence: 99%

Photoacoustic qualitative classification of blood glucose with multiple factors based on BP neural network

Xiong¹,

Peng²,

Wu³

et al. 2022

Optics in Health Care and Biomedical Optics XII

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In this work, the photoacoustic detection of blood glucose with the interference of multiple factors was studied. A set of photoacoustic detection system of blood glucose was established, in which the interference of multiple factors including the laser energy, concentration, temperature, flow velocity and detection distance were combined into. Under different conditions of multiple factors, the time-resolved photoacoustic signals and peak-to-peak values of blood samples were all obtained. To accurately classify the concentration of blood glucose samples, back propagation (BP) neural network was employed to train the photoacoustic peak-to-peak values and the multiple factors. In BP neural network, five different Arabic numerals from 1 to 5 were labeled to denote five kinds of blood glucose levels ranged from2mmol/Lto14mmol/L. The photoacoustic peak-to-peak values, laser energy, temperature, flow velocity and detection distance were used as the input data, the labels denoted different concentrations were used as the output data. Meanwhile, the effects of neurons number in hidden layer and learning factor on the classification accuracy of blood glucose level were investigated. Under the optimal parameters of BP neural network, the accuracy of classifying concentration of blood glucose level reached 85.6% for the test blood glucose samples. Compared with the classification accuracy (71.2%) of blood glucose level based on support vector machine (SVM) algorithm, it is demonstrated that the photoacoustic spectroscopy combined with BP neural network has a good performance in qualitative classification of blood glucose under the interference of multiple factors.

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Section: Introductionmentioning

confidence: 99%

Photoacoustic qualitative classification of blood glucose with multiple factors based on BP neural network

Xiong¹,

Peng²,

Wu³

et al. 2022

Optics in Health Care and Biomedical Optics XII

View full text Add to dashboard Cite

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Noninvasive Spectroscopic Detection of Blood Glucose and Analysis of Clinical Research Status

Man

2022

Journal of Healthcare Engineering

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Frequent measurement of blood glucose concentration in diabetic patients is an important means for diabetes control. Blood glucose monitoring with noninvasive detection technology can not only avoid the pain of patients and eliminate the harm of some biological materials for measuring glucose in vivo but also improve the frequency of detection, so as to control blood glucose concentration more closely. Traditional blood glucose detection methods are invasive and have some limitations. In this study, the significance of noninvasive blood glucose testing was analyzed and was pointed out that noninvasive blood glucose testing can monitor the blood glucose concentration of patients and relieve the pain of patients. Then, this study analyzed the spectral detection methods of noninvasive blood glucose, including conservation of energy metabolism, near infrared spectroscopy, and other spectral detection methods. Finally, this study made a comprehensive analysis of the domestic and international clinical application of noninvasive glucose spectrum monitoring and summarized the clinical application status of noninvasive glucose spectrum monitoring.

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Non-invasive blood glucose in vivo detection based on photoacoustic spectroscopy

Cited by 2 publications

References 0 publications

Photoacoustic qualitative classification of blood glucose with multiple factors based on BP neural network

Photoacoustic qualitative classification of blood glucose with multiple factors based on BP neural network

Noninvasive Spectroscopic Detection of Blood Glucose and Analysis of Clinical Research Status

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