高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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
  • [1] JAVAID A Q, ASHOURI H, TRIDANDAPANI S, et al. Elucidating the hemodynamic origin of ballistocardiographic forces: Toward improved monitoring of cardiovascular health at home[J]. IEEE Journal of Translational Engineering in Health and Medicine, 2016, 4: 1900208. doi: 10.1109/jtehm.2016.2544752
    [2] BRUSER C, STADLTHANNER K, DE WAELE S, et al. Adaptive beat-to-beat heart rate estimation in ballistocardiograms[J]. IEEE Transactions on Information Technology in Biomedicine, 2011, 15(5): 778–786. doi: 10.1109/TITB.2011.2128337
    [3] SHIN J H, HWANG S H, CHANG M H, et al. Heart rate variability analysis using a ballistocardiogram during Valsalva manoeuvre and post exercise[J]. Physiological Measurement, 2011, 32(8): 1239–1264. doi: 10.1088/0967-3334/32/8/015
    [4] YAO Yang, SHIN S, MOUSAVI A, et al. Unobtrusive estimation of cardiovascular parameters with limb ballistocardiography[J]. Sensors, 2019, 19(13): 2922. doi: 10.3390/s19132922
    [5] SCARBOROUGH W R and TALBOT S A. Proposals for ballistocardiographic nomenclature and conventions: Revised and extended report of committee on ballistocardiographic terminology[J]. Circulation, 1956, 14(3): 435–450. doi: 10.1161/01.CIR.14.3.435
    [6] JAVAID A Q, WIENS A D, FESMIRE N F, et al. Quantifying and reducing posture-dependent distortion in ballistocardiogram measurements[J]. IEEE Journal of Biomedical and Health Informatics, 2015, 19(5): 1549–1556. doi: 10.1109/JBHI.2015.2441876
    [7] 崔晓雪, 成忠, 顾晔. 高血压合并糖尿病患者血压变异性与心率变异性的相关性[J]. 中国动脉硬化杂志, 2018, 26(6): 617–620. doi: 10.3969/j.issn.1007-3949.2018.06.014

    CUI Xiaoxue, CHENG Zhong, and GU Ye. Correlation between blood pressure variability and heart rate variability in patients with hypertension and diabetes mellitus[J]. Chinese Journal of Arteriosclerosis, 2018, 26(6): 617–620. doi: 10.3969/j.issn.1007-3949.2018.06.014
    [8] MALIK J, LO Y L, and WU H T. Sleep-wake classification via quantifying heart rate variability by convolutional neural network[J]. Physiological Measurement, 2018, 39(8): 085004. doi: 10.1088/1361-6579/aad5a9
    [9] KIM H G, CHEON E J, BAI D S, et al. Stress and heart rate variability: A meta-analysis and review of the literature[J]. Psychiatry Investigation, 2018, 15(3): 235–245. doi: 10.30773/pi.2017.08.17
    [10] 赵荣建, 汤敏芳, 陈贤祥, 等. 基于光纤传感的生理参数监测系统研究[J]. 电子与信息学报, 2018, 40(9): 2182–2189. doi: 10.11999/JEIT170894

    ZHAO Rongjian, TANG Minfang, CHEN Xianxiang, et al. Research of physiological monitoring system based on optical fiber sensor[J]. Journal of Electronics and Information Technology, 2018, 40(9): 2182–2189. doi: 10.11999/JEIT170894
    [11] KENRY, YEO J C, and LIM C T. Emerging flexible and wearable physical sensing platforms for healthcare and biomedical applications[J]. Microsystems & Nanoengineering, 2016, 2: 16043. doi: 10.1038/micronano.2016.43
    [12] TANG Shihao, LIU Huafeng, YAN Shitao, et al. A high-sensitivity MEMS gravimeter with a large dynamic range[J]. Microsystems & Nanoengineering, 2019, 5: 45. doi: 10.1038/s41378-019-0089-7
    [13] ZHAO Mingmin, YUE Shichao, KATABI D, et al. Learning sleep stages from radio signals: A conditional adversarial architecture[C]. The 34th International Conference on Machine Learning, Sydney, Australia, 2017: 4100–4109.
    [14] 杨昭, 杨学志, 霍亮, 等. 抗运动干扰的人脸视频心率估计[J]. 电子与信息学报, 2018, 40(6): 1345–1352. doi: 10.11999/JEIT170824

    YANG Zhao, YANG Xuezhi, HUO Liang, et al. Heart rate estimation from face videos against motion interference[J]. Journal of Electronics &Information Technology, 2018, 40(6): 1345–1352. doi: 10.11999/JEIT170824
    [15] JIAO Changzhe, SU Boyu, LYONS P, et al. Multiple instance dictionary learning for beat-to-beat heart rate monitoring from ballistocardiograms[J]. IEEE Transactions on Biomedical Engineering, 2018, 65(11): 2634–2648. doi: 10.1109/TBME.2018.2812602
    [16] NAGURA M, MITSUKURA Y, KISHIMOTO T, et al. An estimation of heart rate variability from ballistocardiogram measured with bed leg sensors[C]. 2018 IEEE International Conference on Industrial Technology (ICIT), Lyon, France, 2018: 2005–2009. doi: 10.1109/ICIT.2018.8352495.
    [17] 梁帆, 孟晓风, 余旸. 基于二阶伏特拉级数模型的心脏运动信号快速最小二乘估计[J]. 电子与信息学报, 2013, 35(3): 639–644. doi: 10.3724/SP.J.1146.2012.00866

    LIANG Fan, MENG Xiaofeng, and YU Yang. Second order volterra series model based fast least square method for heart motion prediction[J]. Journal of Electronics &Information Technology, 2013, 35(3): 639–644. doi: 10.3724/SP.J.1146.2012.00866
    [18] FREY B J and DUECK D. Clustering by passing messages between data points[J]. Science, 2007, 315(5814): 972–976. doi: 10.1126/science.1136800
    [19] PAN Jiapu and TOMPKINS W J. A real-time QRS detection algorithm[J]. IEEE Transactions on Biomedical Engineering, 1985, BME-32(3): 230–236. doi: 10.1109/TBME.1985.325532
  • 加载中
图(7) / 表(3)
计量
  • 文章访问数:  1582
  • HTML全文浏览量:  858
  • PDF下载量:  144
  • 被引次数: 0
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

目录

    /

    返回文章
    返回