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基于ECG信号的高精度血糖监测

李婷 叶松 李景振 马菁菁 陆瑶芃 洪培涛 聂泽东

李婷, 叶松, 李景振, 马菁菁, 陆瑶芃, 洪培涛, 聂泽东. 基于ECG信号的高精度血糖监测[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2021.01.12.009
引用本文: 李婷, 叶松, 李景振, 马菁菁, 陆瑶芃, 洪培涛, 聂泽东. 基于ECG信号的高精度血糖监测[J]. 工程科学学报. doi: 10.13374/j.issn2095-9389.2021.01.12.009
LI Ting, YE Song, LI Jing-zhen, MA Jing-jing, LU Yao-peng, HONG Pei-tao, NIE Ze-dong. High accuracy blood glucose monitoring based on ECG signals[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2021.01.12.009
Citation: LI Ting, YE Song, LI Jing-zhen, MA Jing-jing, LU Yao-peng, HONG Pei-tao, NIE Ze-dong. High accuracy blood glucose monitoring based on ECG signals[J]. Chinese Journal of Engineering. doi: 10.13374/j.issn2095-9389.2021.01.12.009

基于ECG信号的高精度血糖监测

doi: 10.13374/j.issn2095-9389.2021.01.12.009
基金项目: 国家重点研发计划资助项目(2018YFC2001002);深圳市基础研究资助项目(JCYJ20180507182231907)
详细信息
    通讯作者:

    E-mail:zd.nie@siat.ac.cn

  • 中图分类号: R587.1;TN911.7

High accuracy blood glucose monitoring based on ECG signals

More Information
  • 摘要: 连续血糖监测在糖尿病管理中具有重要的意义。目前糖尿病患者主要通过指尖采血或植入式微创传感器监测血糖,但上述方法存在疼痛、成本昂贵、易感染等问题,因此,无创监测是实现连续血糖监测的理想技术。本文利用心电(ECG)信号,提出了一种血糖水平无创监测的方法:通过获取12名志愿者共60 d 756160个ECG周期信号,利用递归滤波器实现ECG信号的滤波,并采用卷积神经网络和长短期记忆网络相结合(CNN-LSTM)的方法,实现了血糖水平的十分类监测,并通过实验探索了个体建模和群体建模2种建模方式的差异。结果表明,在个体建模和群体建模的条件下,血糖监测精确率分别约达到80%和88%。其中群体建模10分类的F1值可达到0.95、0.88、0.91、0.85、0.92、0.88、0.86、0.86、0.87和0.86。研究表明,本文提出的基于ECG的无创血糖监测方法为实现血糖水平的实时、精准监测提供了一种有力的理论支撑与技术指导。

     

  • 图  1  ECG数据采集实验图

    Figure  1.  ECG data acquiring experiment

    图  2  一个ECG信号周期示意图

    Figure  2.  ECG signal cycle diagram

    图  3  ECG信号滤波前后图像。(a)未滤波的ECG信号;(b)IIR滤波器去噪后的ECG信号

    Figure  3.  Images of ECG signals before and after filtering: (a) unfiltered ECG signal; (b) ECG signal followed by IIR filter

    图  4  不同志愿者在相同血糖水平下的一个ECG信号周期波形示例。(a)BG=5.9 mmol·L−1;(b)BG=8.1 mmol·L−1;(c)BG=10.5 mmol·L−1

    Figure  4.  ECG signal cycle waveforms at the same BG level for different subjects: (a) BG = 5.9 mmol·L−1; (b) BG = 8.1 mmol·L−1; (c) BG = 10.5 mmol·L−1

    表  1  12名志愿者信息分布(人数)

    Table  1.   Quantity of volunteers with different body information

    GenderAge bracketBMI
    MaleFemale≤24(24,40)≥40<18.5 (Low weight)[18.5,23) (Normal weight)≥23 (Overweight)
    57453363
    下载: 导出CSV

    表  2  群体建模血糖分类标签及数据量

    Table  2.   Blood glucose classification labels and data volumes upon group modeling

    Blood glucose classification/(mmol·L−1)LabelsData
    sizeRation/%
    ≤5.607016410.1
    >5.6 and ≤6.217542410.9
    >6.2 and ≤6.62667659.6
    >6.6 and ≤7.237524710.8
    >7.2 and ≤7.84663469.5
    >7.8 and ≤8.45612728.9
    >8.4 and ≤9.16688239.9
    >9.1 and ≤10.47684649.9
    >10.4 and ≤14.98662929.5
    >14.997561610.9
    下载: 导出CSV

    表  3  CNN‒LSTM模型参数设置

    Table  3.   Parameter setting of the CNN‒LSTM model

    LayersTypeNeuronsFiltersKernel-sizeStridesPaddingPool-size
    1Conv1d(1,1,700)8310
    2BatchNorm1d(8,1, 698)
    3ReLU(8,1, 698)
    4MaxPool1d(8,1, 698)02
    5Conv1d(8,1, 348)16510
    6BatchNorm1d(16,1, 344)
    7ReLU(16,1, 344)
    8MaxPool1d(16,1, 344)02
    9Conv1d(16,1, 172)32810
    10BatchNorm1d(32,1, 165)
    11ReLU(32,1, 165)
    12MaxPool1d(32,1,165)02
    13Conv1d(32,1, 83)128210
    14BatchNorm1d(128,1, 82)
    15ReLU(128,1, 82)
    16LSTM(128,1, 82)128
    17Fully-connected(1,128)
    18Fully-connected(1, 64)
    19Output10
    下载: 导出CSV

    表  4  A1、A2、B1和B2分别进行个体建模性能评估

    Table  4.   Individual modeling performance evaluations for A1, A2, B1, and B2

    VolunteerPrecisionRecallF1-score
    A10.790.790.79
    A20.800.800.80
    B10.810.790.79
    B20.860.860.86
    下载: 导出CSV

    表  5  群体建模下的血糖监测混淆矩阵

    Table  5.   Confusion matrix for blood glucose prediction under group modeling

    LabelsPredict 0Predict 1Predict 2Predict 3Predict 4Predict 5Predict 6Predict 7Predict 8Predict 9
    0108345910000000
    13710213529000000
    2935109331010000
    31090989263400000
    40002011194712112
    5001065945772840
    60000341010992450
    7000020199668771
    8000020643982105
    90000510243271051
    下载: 导出CSV

    表  6  血糖监测模型性能评估

    Table  6.   Performance evaluation of the proposed glucose prediction model

    LabelsPrecisionRecallF1-score
    00.950.940.95
    10.860.910.88
    20.880.930.91
    30.910.800.85
    40.910.930.92
    50.940.830.88
    60.880.850.86
    70.870.840.86
    80.870.860.87
    90.800.940.86
    下载: 导出CSV

    表  7  血糖监测模型对比

    Table  7.   Comparison of glucose prediction models

    Related workClassificationUsing signalsModeling methodModelPrecision/%
    Literature[16]6ECG+PPGIndividual modelingELM83.5
    CNN81.2
    Fractional order system77.3
    This paper10ECGIndividual modelingCNN‒LSTM81.5
    Group modelingCNN‒LSTM88.4
    下载: 导出CSV
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  • 收稿日期:  2021-01-12
  • 网络出版日期:  2021-08-30

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