|
|
|
|
|
|
| Study on Non-Invasive Blood Glucose Detection Technology Based on Time Frequency Domain Analysis |
| CHEN Jian-hong, REN Jun-yi, YANG Jia, GUO Ya-ya, QIAO Wei-dong |
Faculty of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China
|
|
|
|
|
Abstract The non-invasive blood glucose detection technique is an indirect method of measuring glucose levels in the blood, which is safe, fast, and non-invasive without damaging human tissues, breaking the limitations of traditional blood glucose detection, and has important research value. The photoplethysmography signal, containing various physiological and pathological information, is widely used in various clinical studies and is also the focus of attention in the current implementation of the non-invasive glucose detection technique. Current studies of non-invasive blood glucose detection based on photoplethysmography signals have only considered the contribution to system modelling when the time or frequency domains act alone. Although the time domain analysis of the signal can describe the variation of the PPG signal amplitude with time, it cannot visually reflect the energy distribution of the PPG signal frequency. Therefore, the signal analysis of a single domain cannot fully express the PPG signal, which leads to information loss. When using frequency domain analysis to extract the signal spectrum, it is necessary to use all the time domain information of the signal, which is a global transformation and may result in the loss of signal characteristics at a specific time or in a specific frequency band. In summary, this paper proposes a new method for non-invasive blood glucose detection based on the integrated time-frequency domain analysis of photoplethysmography (PPG), using a parallel time-frequency domain method to consider the association between the photoplethysmography signal and blood glucose, a cluster analysis method is used to extract representative waveforms in the time domain of the PPG signal, analyze the correlation between the waveform features and blood glucose, and determine the time domain feature parameters of the waveform. On this basis, the pulse waveform time domain signal is converted to the frequency domain using the Fast Fourier Transform, and the spectral information is studied using principal component analysis to establish the frequency domain characteristic quantities. The BP neural network-based non-invasive blood glucose detection model is constructed by extracting the time-frequency domain feature parameters from the waveform signals obtained through the Oral Glucose Tolerance Test (OGTT) and using the invasive blood glucose concentration detected in real time as a reference. At the same time, in order to improve the accuracy of the model and achieve model optimization, a genetic algorithm is applied to the model for the second correction, and the final MAE and RMSE of the test set reach 1.13 and 1.42 mmol·L-1. The results of Parker's CEG show that the prediction results in the A and B regions accounted for 80.3% and 19.7%, respectively, which indicates that the method has good prediction accuracy and provides a theoretical basis for the feasibility of daily non-invasive blood glucose monitoring. It is beneficial to improve the detection and monitoring systems for diabetes, to judge the condition better comprehensively, and to prevent, guide, and treat diabetes promptly.
|
|
Received: 2022-06-07
Accepted: 2022-11-04
|
|
|
|
[1] SHI Yun, SHEN Min, HSU Hsiang-ting, et al(施 云，沈 敏，徐湘婷，等). Chin. J. Endocrinol. Metabol.(中华内分泌代谢杂志), 2019, 35(5): 383.
[2] CHEN Li-ming, ZHOU Jian(陈莉明, 周 健). Chinese Journal of Diabetes(中华糖尿病杂志), 2018, 10(11): 697.
[3] HU Min, XIAO Xia, SONG Hang, et al(胡 敏, 肖 夏, 宋 航, 等). Laser & Optoelectronics Progress(激光与光电子学进展), 2020, 57(23): 230401.
[4] SUN Kai, ZHOU Hua, YANG Ying-kun, et al(孙 凯, 周 华, 杨膺琨, 等). Chinese Journal of Lasers(中国激光), 2018, 45(2): 0207003.
[5] Habbu S, Dale M, Ghongade R. Sdhan, 2019, 44(6): 135.
[6] LI Hong, SONG Fan-lei, SONG Li(栗 红, 宋范蕾, 宋 莉). China Science and Technology Information(中国科技信息), 2021，(16): 105.
[7] Qananwah Q, Dagamseh A, Alquran H, et al. Physical & Engineering Sciences in Medicine, 2020, 43(4): 1207.
[8] ZHANG Jian-feng, CHEN Guang-hua, GAO Bo(张剑锋, 陈光华, 高 博). Electronic Measurement Technology(电子测量技术), 2019, 42(13): 117.
[9] Hina A, Nadeem H, Saadeh W. 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019.
[10] LIU Guang-da, CAI Jing, SUN Mao-lin, et al(刘光达, 蔡 靖, 孙茂林, 等). Journal of Jilin University(Information Science Edition)[吉林大学学报(信息科学版)], 2015, 33(1): 52.
[11] Zhang Y, Wang Z. 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2017: 594.
[12] CHEN Jian-hong, LIN Zhi-qiang, SUN Chao-yue(陈剑虹, 林志强, 孙超越). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(8): 2378.
[13] LI Ling-fang, HUANG Wen-pei, HU Wei-jian(李灵芳, 黄文培, 胡伟健). Software Guide(软件导刊), 2016, 15(6): 204.
[14] ZHOU Shi-bo, XU Wei-xiang, XU Liang-kun(周世波, 徐维祥, 徐良坤). Chinese Journal of Scientific Instrument(仪器仪表学报), 2019, 40(4): 137.
[15] Priyadarshini R G, Kalimuthu M, Nikesh S, et al. IOP Conference Series: Materials Science and Engineering, 2021, 1084(1): 012031.
[16] WU Shun-xi, HUANG Wen-jin(吴顺喜, 黄文晋). Practical Electronics(电子制作), 2015, (10): 121.
[17] FENG Chen-xing, YANG Ying, XU Guo-qing(冯晨星, 杨 颖, 徐国卿). Beijing Biomedical Engineering(北京生物医学工程), 2019, 38(1): 102.
[18] SI Yong-sheng, CAO Shan-shan, ZHANG Xiao-xue, et al(司永胜, 曹珊珊, 张晓雪, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2021, 52(10): 377.
[19] LI Yong, QIN Cai-jie(李 勇, 秦彩杰). Journal of Huaihua University(怀化学院学报), 2021, 40(5): 57.
[20] WANG Zhen-zhou, NIE Ya-ning, YU Ping-ping(王震洲, 聂亚宁, 于平平). Journal of Electronic Measurement and Instrumentation(电子测量与仪器学报), 2019, 33(11): 73.
|
| [1] |
LI Yang1, 2, LI Cui-ling2, 3, WANG Xiu2, 3, FAN Peng-fei2, 3, LI Yu-kang2, ZHAI Chang-yuan1, 2, 3*. Identification of Cucumber Disease and Insect Pest Based on
Hyperspectral Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 301-309. |
| [2] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
| [3] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [4] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
| [5] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
| [6] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
| [7] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
| [8] |
CHEN Wen-jing, XU Nuo, JIAO Zhao-hang, YOU Jia-hua, WANG He, QI Dong-li, FENG Yu*. Study on the Diagnosis of Breast Cancer by Fluorescence Spectrometry Based on Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2407-2412. |
| [9] |
XIA Chen-zhen1, 2, 3, JIANG Yan-yan4, ZHANG Xing-yu1, 2, 3, SHA Ye5, CUI Shuai1, 2, 3, MI Guo-hua5, GAO Qiang1, 2, 3, ZHANG Yue1, 2, 3*. Estimation of Soil Organic Matter in Maize Field of Black Soil Area Based on UAV Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2617-2626. |
| [10] |
HU Wen-feng1, 2, TANG Wei-hao1, LI Chuang1, WU Jing-jin1, MA Qing-fen1, LUO Xiao-chuan1, WANG Chao2, TANG Rong-nian1*. Estimating Nitrogen Concentration of Rubber Leaves Based on a Hybrid Learning Framework and Near-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2050-2058. |
| [11] |
FENG Ying-chao1, HUANG Yi-ming2*, LIU Jin-ping1, JIA Chen-peng2, CHEN Peng1, WU Shao-jie2*, REN Xu-kai3, YU Huan-wei3. On-Line Monitoring of Laser Wire Filling Welding Process Based on Emission Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1927-1935. |
| [12] |
JIANG Chuan-li1, ZHAO Jian-yun1, 2*, DING Yuan-yuan1, ZHAO Qin-hao1, MA Hong-yan1. Study on Soil Water Retrieval Technology of Yellow River Source Based on SPA Algorithm and Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1961-1967. |
| [13] |
HU Hui-qiang1, WEI Yun-peng1, XU Hua-xing1, ZHANG Lei2, MAO Xiao-bo1*, ZHAO Yun-ping2*. Identification of the Age of Puerariae Thomsonii Radix Based on Hyperspectral Imaging and Principal Component Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1953-1960. |
| [14] |
SU Yun-peng, HE Chun-jing, LI Ang-ze, XU Ke-mi, QIU Li-rong, CUI Han*. Ore Classification and Recognition Based on Confocal LIBS Combined With Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(03): 692-697. |
| [15] |
YANG Cheng-en1, SU Ling2, FENG Wei-zhi1, ZHOU Jian-yu1, WU Hai-wei1*, YUAN Yue-ming1, WANG Qi2*. Identification of Pleurotus Ostreatus From Different Producing Areas Based on Mid-Infrared Spectroscopy and Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 577-582. |
|
|
|
|
