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基于压电陶瓷传感器的非接触式精准逐拍心率提取方法研究

方震 白忠瑞 陈贤祥 夏攀 何征岭 赵荣建

方震, 白忠瑞, 陈贤祥, 夏攀, 何征岭, 赵荣建. 基于压电陶瓷传感器的非接触式精准逐拍心率提取方法研究[J]. 电子与信息学报, 2021, 43(5): 1472-1479. doi: 10.11999/JEIT200045
引用本文: 方震, 白忠瑞, 陈贤祥, 夏攀, 何征岭, 赵荣建. 基于压电陶瓷传感器的非接触式精准逐拍心率提取方法研究[J]. 电子与信息学报, 2021, 43(5): 1472-1479. doi: 10.11999/JEIT200045
Zhen FANG, Zhongrui BAI, Xianxiang CHEN, Pan XIA, Zhengling HE, Rongjian ZHAO. Unconstrained Accurate Beat-to-beat Heart Rate Extraction Based on Piezoelectric Ceramics Sensor[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1472-1479. doi: 10.11999/JEIT200045
Citation: Zhen FANG, Zhongrui BAI, Xianxiang CHEN, Pan XIA, Zhengling HE, Rongjian ZHAO. Unconstrained Accurate Beat-to-beat Heart Rate Extraction Based on Piezoelectric Ceramics Sensor[J]. Journal of Electronics & Information Technology, 2021, 43(5): 1472-1479. doi: 10.11999/JEIT200045

基于压电陶瓷传感器的非接触式精准逐拍心率提取方法研究

doi: 10.11999/JEIT200045
基金项目: 国家重点研发计划(2016YFC1304302, 2018YFC2001802, 2018YFC2001101)
详细信息
    作者简介:

    方震:男,1976年生,研究员,博士生导师,研究方向为可穿戴技术

    白忠瑞:男,1996年生,硕士生,研究方向为非接触生命信息感知技术

    陈贤祥:男,1979年生,副研究员,硕士生导师,研究方向为可穿戴技术

    夏攀:男,1994年生,硕士生,研究方向为可穿戴技术

    何征岭:男,1993年生,博士生,研究方向为健康物联网技术

    赵荣建:男,1985年生,博士后,研究方向为生命信息感知技术

    通讯作者:

    方震 zfang@mail.ie.ac.cn

  • 中图分类号: TP212.3; R318.04

Unconstrained Accurate Beat-to-beat Heart Rate Extraction Based on Piezoelectric Ceramics Sensor

Funds: The National Key Research and Development Project (2016YFC1304302, 2018YFC2001802, 2018YFC2001101)
  • 摘要: 心冲击图(BCG)可用于无接触式地监测生命体征。在BCG的逐拍心率提取中,较低的平均绝对误差对于精确地获取用户的心率变异性(HRV)指标具有重要意义。为解决目前大多数方法在逐拍心率计算精度方面的不足,该文设计了一种基于压电陶瓷的心冲击信号采集系统。通过采用合适的传感器外壳结构和采样频率,增加传感器的灵敏度和BCG的时间分辨率;通过对比不同的BCG处理方法并找到BCG中最适合提取精准逐拍心动周期的成分;同时该文提出一种采用AP聚类的自适应模板匹配算法,以准确提取心动周期信息。对15名受试者共5741次心跳数据进行分析,结果显示逐拍心动周期的平均误差为0.48%,平均绝对误差为3.78 ms,心跳覆盖率在97%以上,优于其他同类工作。
  • 图  1  BCG不同成分提取结果

    图  2  BCG模板聚类与BCG模板

    图  3  非接触式逐拍心动周期检测准确度验证实验示意图

    图  4  不同频带滤波的BCG模板匹配计算结果

    图  5  ECG-RR间期与不同频率滤波的逐拍BCG-JJ间期折线图

    图  6  受试者1的逐拍RR间期与JJ间期Bland-Altman图

    图  7  各成分误差分布箱型图

    表  1  受试者1的数据处理结果对比

    预处理方式心跳覆盖率(%)平均绝对误差(ms)平均误差(%)
    3~24 Hz带通滤波100.0014.101.69
    db6小波重构100.0011.081.33
    8~24 Hz带通滤波98.603.460.41
    二次差分99.165.130.61
    下载: 导出CSV

    表  2  不同预处理方式下逐拍心率提取性能

    受试者编号心跳次数3~24 Hz带通滤波db6小波重构8~24 Hz带通滤波二次差分
    COV(%)MAE(ms)COV(%)MAE(ms)COV(%)MAE(ms)COV(%)MAE(ms)
    1351100.0014.10100.0011.0898.863.4699.155.13
    2412100.0013.60100.0012.4299.034.6699.035.89
    345099.5614.7299.5613.2899.565.4499.116.71
    4353100.0012.58100.0013.8097.732.9294.334.72
    5384100.0011.68100.0010.1298.963.1298.964.22
    637399.469.2298.9212.0397.322.3496.785.93
    740599.5110.4699.5110.1098.524.1798.524.93
    8342100.0013.34100.0010.32100.004.5597.084.22
    9465100.0012.9799.149.9895.703.9493.556.52
    10281100.0013.53100.0011.1699.293.0299.295.54
    1137194.6113.8894.6112.0194.013.9393.124.01
    1235897.7714.2597.2113.2896.103.1296.103.54
    1338298.4313.9598.4314.8897.384.2178.533.03
    14378100.008.2297.8810.2597.883.2298.125.90
    1543699.5410.0999.549.3496.334.5398.624.06
    平均382.799.2612.4498.9911.6097.783.7896.024.96
    下载: 导出CSV

    表  3  BCG逐拍心率提取方法性能对比

    作者Bruser等人[2]Jiao等人[15]Nagura等人[16]本文
    方法设备/采样率床架-应变片/128 Hz床垫下-四路液压
    传感器/100 Hz
    床腿下-四路压力传感器/200 Hz床垫下-压电陶瓷
    传感器/250 Hz
    算法形状聚类学习模板;
    混合探测
    多示例学习局部极大值计算模板BCG成分分解;相关
    聚类学习模板
    关键参数1 Hz高通滤波;K-means
    聚类特征峰数目N=7;
    0.4~10 Hz带通滤波学习器
    参数T=M=3, β=90
    1~8.5 Hz带通滤波;局部
    最大值选取下界t=0.7 s
    8~24 Hz带通滤波;
    AP聚类相似度s=ρ–1
    数据量16×26 min40×5 min4×10 min15×5 min
    性能覆盖率(%)95.9487.1098.50
    MAE(ms)16.61≈30.009.803.78
    平均误差(%)1.794.07≈1.300.48
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-01-13
  • 修回日期:  2020-11-30
  • 网络出版日期:  2020-12-04
  • 刊出日期:  2021-05-18

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