摘要
无创血糖检测技术是一种间接测量血液中葡萄糖含量的方法,其不损伤人体组织具有安全、快捷、无创的特点,打破了传统血糖检测的局限性,具有重要的研究价值。光电容积脉搏波信号因携带多种生理病理信息,被广泛应用于各种临床研究,也是目前实现无创血糖检测技术的重点关注对象。目前基于光电容积脉搏波信号的无创血糖检测研究,仅考虑了时间域或频率域单独作用时对系统建模的贡献。信号的时域分析虽能描述PPG信号幅值随时间的变化,却无法直观反映PPG信号频率的能量分布,因此单一域的信号分析不能全面表达PPG信号,从而导致信息丢失。采用频域分析方法提取信号频谱时,需要利用信号的全部时域信息,是一种全局的变换,可能会造成特定时间或特定频率段内的信号特性丢失。提出了一种基于光电容积脉搏波(PPG)时频域综合分析的无创血糖检测新方法,采用时域-频域并行法综合考量光电容积脉搏波信号与血糖间的联系。以时域分析为基准,利用聚类分析法在PPG信号时域中提取代表波形,分析波形特征点与血糖相关性,确定波形时域特征参数。在此基础上,利用快速傅里叶变换将脉搏波时域信号转换至频率域,采取主成分分析手段研究频谱信息,确立频域特征量。通过口服葡萄糖耐糖实验(OGTT)对获取的波形信号提取时频域特征参数,以实时检测的有创血糖浓度值作为参考,构建基于BP神经网络的无创血糖检测模型,同时为提升模型精度实现模型最优化,应用遗传算法对模型进行二次修正,最终实现模型测试集平均绝对误差(MAE)为1.13 mmol·L^(-1),均方根误差(RMSE)为1.42 mmol·L^(-1)。Parkers共识网络栅格(Parkers CEG)评估结果显示:在A区与B区的预测结果分别占80.3%、19.7%,实验结果表明该方法具有良好的预测精度,为实现日常血糖无创监测可行性提供了理论基础及可靠性依据。有助于完善糖尿病的检测与监测体系,更好地全面判断病情,及时预防、指导、治疗糖尿病。
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.
作者
陈剑虹
任军怡
杨佳
郭亚亚
乔卫东
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)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2024年第2期318-324,共7页
Spectroscopy and Spectral Analysis
基金
国家重点研发计划重大科学仪器设备开发重点专项(2017YFF0104403)
陕西省重点研发计划项目(2020SF-427)资助。
关键词
无创血糖检测
光电容积脉搏波
时频域综合分析
机器学习
Non-invasive blood glucose detection
PPG signals
Time domain and frequency domain analysis
Machine learning
