基于光纤传感的生理参数监测系统研究

赵荣建; 汤敏芳; 陈贤祥; 杜利东; 曾华林; 赵湛; 方震

doi:10.11999/JEIT170894

基于光纤传感的生理参数监测系统研究

doi: 10.11999/JEIT170894

赵荣建^{1, 2},
汤敏芳^{1, 2},
陈贤祥¹,
杜利东¹,
曾华林³,
赵湛^{1, 2},
方震^{1, 2, ,}

1.
中国科学院电子学研究所传感技术国家重点实验室北京 100080
2.
中国科学院大学北京 100049
3.
中国科学院信息工程研究所北京 100093

基金项目: 北京市自然科学基金(Z16003)，国家重点研发计划(2016YFC1304302)

详细信息

作者简介:
赵荣建：男，1985年生，博士生，研究方向为生命信息感知技术

汤敏芳：女，1996年生，博士生，研究方向为可穿戴式技术

陈贤祥：男，1979年生，副研究员，硕士生导师，研究方向为可穿戴式技术

杜利东：男，1981年生，助理研究员，研究方向为微纳制造技术

曾华林：男，1974年生，副研究员，研究方向为光传感器技术

赵湛：男，1958年生，研究员，博士生导师，研究方向为微纳制造技术、无线传感器网络、生命信息感知与计算

方震：男，1976年生，研究员，博士生导师，研究方向为可穿戴式技术

通讯作者:
方震　 zfang@mail.ie.ac.cn

中图分类号: TP389.1;Q819
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出版历程
- 收稿日期: 2017-09-21
- 修回日期: 2018-06-15
- 网络出版日期: 2018-07-12
- 刊出日期: 2018-09-01

Research of Physiological Monitoring System Based on Optical Fiber Sensor

Rongjian ZHAO^{1, 2},
Minfang TANG^{1, 2},
Xianxiang CHEN¹,
Lidong DU¹,
Hualin ZENG³,
Zhan ZHAO^{1, 2},
Zhen FANG^{1, 2
, ,}

1.
State Key Laboratory of Transducer Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100080, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China

Funds: The Key Project of Beijing Municipal Natural Science Foundation (Z16003), The National Key Research and Development Project of China (2016YFC1304302)

摘要

摘要: 常规生理参数监测系统由于测量时接触皮肤，因此舒适感差、个体依从性差。为解决上述问题，该文基于生理的微弱运动可致光纤微弯曲变形进而致光强度发生变化的原理，研制了新型的基于光纤传感的生理参数监测系统。该系统通过光探测器自适应地检测细小的光强变化获得心冲击图(BCG)信号，利用信号处理算法获取心率、呼吸率和体动等信息；把光纤嵌入床垫或坐垫设计为三明治结构，既保护了光纤又增强了系统的可靠性和稳定性；采用蛇形返折走线将光纤均匀地分布在垫子中间，使系统具有高灵敏度。通过多家医院临床标准方法对比测试可得在95%的置信区间(±1.96SD)内该系统心率均值误差为–0.26±2.80次/min，与标准值之间的相关性为0.9984；呼吸率均值误差为0.41±1.49次/min，与标准值之间的相关性为0.9971。实验表明，研制的系统可在零负荷的状态下无感进行生理参数测量，在健康医疗领域具有广泛的应用前景。
- 光纤传感器 /
- 心冲击图 /
- 心率 /
- 呼吸率
Abstract: Conventionally, the physiological monitoring system obtains singnal by electrode or bandage which is connected with skins and has disadvantages such as: uncomfortable and bad compliance to users. In order to overcome those problems, a new physiological monitoring system, which is based on the principle that micro bend of optical-fiber induced by weak movement of physiology can change the light intensity to get BallistoCardioGram (BCG) signal, is developed. In such system, the respiration rate, heart rate and body movement are obtained by self-adaption detecting the tiny variation of light intensity. In order to protect fiber and enhance the stability and reliability of system, the fiber is embedded into mattress or cushion with a sandwich structure. Simultaneously, it makes the system have high sensitivity that the fiber is uniformly routed with serpentine-curve shape in the middle of mattress or cushion. It is illustrated by the measurement in several hospitals that the mean error of heart rate is –0.26±2.80 times/min within 95% the confidence interval (±1.96SD) with a correlation 0.9984 to the standard values. It is exhibited as well that the mean error respiration rate is 0.41±1.49 times/min within 95% the confidence interval (±1.96SD) with a correlation 0.9971 to the standard values. It is suggested that the developed system can be senselessly used under zero load and is promised in future.
- Fiber optic sensor /
- Ballistocardiogram /
- Heart rate /
- Respiration rate

HTML全文

图 1 微弯曲光纤式传感器结构图

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图 2 微弯曲光纤传导损耗图

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图 3 系统结构框图

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图 4 光纤垫子的结构框图

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图 5 光纤采集系统的电路图

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图 6 光纤采集控制反馈环

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图 7 智能光纤睡垫AD采集的原始BCG信号

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图 8 经过滤波后的BCG信号

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图 9 呼吸率算法流程图

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图 10 心率算法流程图

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图 11 原型机照片

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图 12 后台记录的某用户连续20 min的心率和呼吸率图

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图 13 心率测量相关系数图和心率Bland-Altman图

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图 14 呼吸率测量相关系数图和呼吸率Bland-Altman图

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参考文献(18)

DOVSKY V, et al. Smart helmet: Wearable multichannel ECG and EEG[J]. IEEE Journal of Translational Engineering in Health and Medicine, 2016: 270011 doi: 10.1109/JTEHM.2016.2609927

VROSENBERG W V, CHANWIMALUEANG T, GOVER doi: 10.1109/JTEHM.2016.2609927

HASSAN M A, MALIK A S, FOFI D, et al. Heart rate estimation using facial video: A review[J]. Biomedical Signal Processing and Control, 2017, 38(8): 346–360 doi: 10.1016/j.bspc.2017.07.004

LIU M, JIANG F, JIANG H, et al. Low-power, noninvasive measurement system for wearable ballistocardiography in sitting and standing positions[J]. Computers in Industry, 2017, 91(10): 24–32 doi: 10.1016/j.compind.2017.05.005

FAUSTMAN D L. Methods of treating and diagnosing disease using biomarkers for bcg therapy: WIPO, WO2017059132A1[P]. 2017-04-06.

PETRINI V P, MATTIA V D M D, LEO A D L D, et al. Contactless Monitoring of Respiratory Activity Using Electromagnetic Waves for Ambient Assisted Living Framework: Feasibility Study and Prototype Realization[M]. London, UK, The Institution of Engineering and Technology, 2017: 30–40.

CHEN Z, LAU D, TEO J T, et al. Simultaneous measurement of breathing rate and heart rate using a microbend multimode fiber optic sensor[J]. Journal of Biomedical Optics, 2014, 19(5): 057001 doi: 10.1117/1.JBO.19.5.057001

YANG X, CHEN Z, ELVIN C S M, et al. Textile fiber optic microbend sensor used for heartbeat and respiration monitoring[J]. Sensors Journal IEEE, 2015, 15(2): 757–761 doi: 10.1109/JSEN.2014.2353640

DEEPU C J, CHEN Z, JU T T, et al. A smart cushion for real-time heart rate monitoring[C]. 2012 IEEE Biomedical Circuits and Systems Conference, Hsinchu, China, 2012: 53–56.

ZHU Y, ZHANG H, JAYACHANDRAN M, et al. Ballistocardiography with fiber optic sensor in headrest position: A feasibility study and a new processing algorithm[C]. Engineering in Medicine and Biology Society (EMBC), 35th Annual International Conference of the IEEE EMBS, Osaka, Japan, 2013: 5203–5206.

CHEN Zhihao, TEO J T, NG S H, et al. Monitoring respiration and cardiac activity during sleep using microbend fiber sensor: A clinical study and new algorithm[C]. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, USA, 2014: 5377–5380.

NI Hongbo, HE Mingjie, XU Guxing, et al. Extracting heartbeat intervals using self-adaptive method based on ballistocardiography(BCG)[C]. International Conference on Smart Homes and Health Telematics, Paris, France, 2017: 37–47.

肖玲, 李仁发, 罗娟. 体域网中一种基于压缩感知的人体动作识别方法[J]. 电子与信息学报, 2013, 35(1): 119–125 doi: 10.3724/SP.J.1146.2012.00936

XIAO Ling, LI Renfa, and LUO Juan. Recognition of human activity based on compressed sensing in body sensor networks[J]. Journal of Electronics&Information Technology, 2013, 35(1): 119–125 doi: 10.3724/SP.J.1146.2012.00936

高发荣, 王佳佳, 席旭刚, 等. 基于粒子群优化-支持向量机方法的下肢肌电信号步态识别[J]. 电子与信息学报, 2015, 37(5): 1154–1159 doi: 10.11999/JEIT141083

GAO Farong, WANG Jiajia, XI Xugang, et al. Gait recognition for lower extremity electromyographic signals based on PSO-SVM method[J]. Journal of Electronics&Information Technology, 2015, 37(5): 1154–1159 doi: 10.11999/JEIT141083

ZHAO W, NI H, ZHOU X, et al. Identifying sleep apnea syndrome using heart rate and breathing effort variation analysis based on ballistocardiography[C]. Engineering in Medicine and Biology Society, Milan, Italia, 2015: 4536–4539.

JOSE S K, SHAMBHARKAR C M, and CHUNKATH J. HRV analysis using ballistocardiogram with LabVIEW[C]. 2015 International Conference on Computing and Communications Technologies, Chennai, India, 2015: 128–132.

ROSALES L, SU B Y, SKUBIC M, et al. Heart rate monitoring using hydraulic bed sensor ballistocardiogram[J]. Journal of Ambient Intelligence&Smart Environments, 2017, 9(2): 193–207 doi: 10.3233/AIS-170423

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

NISHYAMA M, MIYAMOTO M, and WATANABE K. Respiration and body movement analysis during sleep in bed using hetero-core fiber optic pressure sensors without constraint to human activity[J]. Journal of Biomedical Optics, 2011, 16(1): 017002 doi: 10.1117/1.3528008

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