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

Chengxin Xiong; Wenping Peng; Junli Wu; Gaoqiang Liang; Bingheng Sun; Tao Liu; Zhong Ren

doi:10.1117/12.2641772

19 December 2022 Photoacoustic qualitative classification of blood glucose with multiple factors based on BP neural network

Chengxin Xiong, Wenping Peng, Junli Wu, Gaoqiang Liang, Bingheng Sun, Tao Liu, Zhong Ren

Author Affiliations +

Proceedings Volume 12320, Optics in Health Care and Biomedical Optics XII; 123201O (2022) https://doi.org/10.1117/12.2641772
Event: SPIE/COS Photonics Asia, 2022, Online Only

Conference Poster

Abstract

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.

Citation Download Citation

Chengxin Xiong, Wenping Peng, Junli Wu, Gaoqiang Liang, Bingheng Sun, Tao Liu, and Zhong Ren "Photoacoustic qualitative classification of blood glucose with multiple factors based on BP neural network", Proc. SPIE 12320, Optics in Health Care and Biomedical Optics XII, 123201O (19 December 2022); https://doi.org/10.1117/12.2641772

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available