基于自适应小波尺度选择的生物雷达呼吸与心跳分离方法

胡锡坤; 金添

doi:10.12000/JR16103

基于自适应小波尺度选择的生物雷达呼吸与心跳分离方法

DOI: 10.12000/JR16103

胡锡坤,
金添^,

(国防科学技术大学电子科学与工程学院长沙 410073)

基金项目:

国家自然科学基金（61271441）

详细信息

作者简介:
胡锡坤(1994-),男,湖北天门人,国防科学技术大学硕士研究生,主要研究方向为超宽带雷达生命信号检测技术。E-mail:xikung_hu@126.com;金添(1980-),男,国防科学技术大学教授,博士生导师,主要从事隐蔽目标雷达成像与检测识别、新型微波传感器机理与系统实现等方面的研究工作。2009年获全国优秀博士学位论文奖,2010年入选教育部"新世纪优秀人才支持计划",2014年获国际无线电科学联盟青年科学家奖。承担国家自然科学基金、武器装备探索等多项课题,获省部级科技进步一等奖1项、二等奖2项。"信号处理与系统"国家精品课程和资源共享课主讲教师,信号处理系列课程国家级教学团队主要成员。已发表论文100余篇,获授权国家发明专利5项,出版专著3部、译著1部、教材1部。E-mail:tianjin@nudt.edu.cn

通讯作者:
金添tianjin@nudt.edu.cn

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出版历程
- 收稿日期: 2016-09-13
- 修回日期: 2016-10-26
- 网络出版日期: 2016-10-28

Adaptive Wavelet Scale Selection-based Method for Separating Respiration and Heartbeat in Bio-radars

Hu Xikun,
Jin Tian^,

(College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China)

Funds:

The National Natural Science Foundation of China (61271441)

摘要

摘要: 当使用生物雷达进行生命体征参数提取时，由于心脏跳动产生的位移形变很小，回波较为微弱，而呼吸带动的胸腔起伏回波强度较大，基于简单傅里叶变换的周期性信息检测，往往无法有效提取心跳信号。采用小波变换的方法可以较好地分离出含有心跳、呼吸运动信息的信号分量，但小波尺度的选择对于不同的场景存在细微差异，影响到了分离的效果。针对这一问题，该文采用Morlet二进小波变换，提出了一种基于信噪比阈值定标的自适应小波尺度选择方法，有效解决了不同场景的呼吸心跳分离问题。最后通过实测结果验证了算法的准确性和可行性。
- 生物雷达 /
- 自适应小波选择 /
- 呼吸和心跳信号分离
Abstract: Extracting periodic heartbeat signals based on the traditional Fourier transform using a noncontact bio-radar is difficult because chest displacements caused by the heart are much smaller than those caused by respiration. Normally, they can be separated using the continuous wavelet transform; however, the miniscule difference of wavelet scale selection under different conditions may influence the separation performance to some extent. To solve this problem, this study proposes a method based on signal-to-noise ratio calibration to adaptively select the Morletdyadic wavelet scales and then separate the heartbeat signal from the respiration one using the selected scales, which can be applied to detect vital signs of different conditions. The experimental results have exhibited the accuracy and feasibility of the proposed method.
- Bio-radar /
- Adaptive wavelet selection /
- Respiration and heartbeat separation

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参考文献(19)

[1]	胡巍. 基于多普勒雷达的非接触式生命体征检测技术研究[D].[博士论文], 中国科学技术大学, 2014:1-5. Hu Wei. Non-contact vital sign detection based on Doppler radar[D].[Ph.D. dissertation], University of Science and Technology of China, 2014:1-5.
[2]	Li Changzhi, Lin Jenshan, and Xiao Yanming. Robust overnight monitoring of human vital signs by a non-contact respiration and heartbeat detector[C]. IEEE EMBS Annual International Conference, New York, NY, USA, 2006:2235-2238.
[3]	Hu Wei, Zhao Zhangyan, Wang Yunfeng, et al.. Noncontact accurate measurement of cardiopulmonary activity using a compact quadrature Doppler radar sensor[J]. IEEE Transactions on Biomedical Engineering, 2014, 61(3):725-735.
[4]	Gu Changzhan and Li Changzhi. Assessment of human respiration patterns via noncontact sensing using Doppler multi-radar system[J]. Sensors, 2015, 15(3):6383-6398.
[5]	Gu Changzhan. Short-Range noncontact sensors for healthcare and other emerging applications:A review[J]. Sensors, 2016, 16(8). Pii:E1169. DOI: 10.3390/s16081169.
[6]	Chen Kun-Mu, Huang Yong, Zhang Jianping, et al.. Microwave life-detection systems for searching humansubjects under earthquake rubble or behind barrier[J]. IEEE Transactions on Biomedical Engineering, 2000, 47(1):105-114.
[7]	Gu Changzhan and Li Changzhi. From tumor targeting to speech monitoring:accurate respiratory monitoring using medical continuous-wave radar sensors[J]. IEEE Microwave Magazine, 2014, 15(4):66-76.
[8]	Fontana R J. Recent system applications of short-pulse ultra-wideband (UWB) technology[J]. IEEE Transactions on Microwave Theory and Techniques, 2004, 52(9):2087-2104.
[9]	Lazaro A, Girbau D, and Villarino R. Analysis of vital signs monitoring using an IR-UWB radar[J]. Progress In Electromagnetics Research, 2010, 100:265-284.
[10]	Tariq A and Ghafouri-Shiraz H. Vital signs detection using Doppler radar and continuous wavelet transform[J]. Journal of Nursing Science, 2011, 10(5):285-288.
[11]	Tariq A and Ghafouri-Shiraz H. Doppler radar vital signs monitoring using wavelet transform[C]. 2010 Antennas & Propagation Conference, Loughborough, 2010:293-296.
[12]	Morgan D and Zierdt M. Novel signal processing techniques for Doppler radar cardiopulmonary sensing[J]. Signal Processing, 2009, 89(1):45-66.
[13]	易丽, 何怡刚, 方葛丰, 等. 基于Morlet小波在雷达信号脉内特征提取中的应用[J]. 计算机应用研究, 2013, 30(1):172-175. Yi Li, He Yigang, Fang Gefeng, et al.. Application of modified Morlet wavelet in feature extraction of radar signal to intra-pulse[J]. Application Research of Computers, 2013, 30(1):172-175.
[14]	薛蕙, 杨仁刚. 基于连续小波变换的非整数次谐波测量方法[J]. 电力系统自动化, 2003, 27(5):49-53. Xue Hui and Yang Rengang. A novel method for non-integer harmonics measurement using continuous wavelet transform[J]. Automation of Electric Power Systems, 2003, 27(5):49-53.
[15]	卢新城, 龚沈光, 周骏, 等. 基于Morlet小波的高分辨信号频谱估计[J]. 武汉理工大学学报(交通科学与工程版), 2002, 27(5):61-74. Lu Xincheng, Gong Shenguang, Zhou Jun, et al.. High resolution signal spectrum estimation based on Morletwavelet[J]. Journal of Wuhan University of Technology (Transportation Science & Engineering), 2002, 27(5):61-74.
[16]	Yang Yushan and Li Yuanyuan. Spectral analysis of geomagnetic data with continuous wavelet transform[C]. International Congress on Image and Signal Processing, 2009:1-4.
[17]	Rueda J L, Juarez C A, and Erlich I. Wavelet-based analysis of power system low-frequency electromechanical oscillations[J]. IEEE Transactions on Power Systems, 2011, 26(3):1733-1743.
[18]	Ball John E and Tolley Alan. Low SNR radar signal detection using the continuous wavelet transform (CWT) and a Morlet wavelet[C]. 2008 IEEE Radar Conference, 2008:1-6.
[19]	Lazaro Antonio, Girbau David, and Villarino Ramon. Techniques for clutter suppression in the presence of body movements during the detection of respiratory activity through UWB radars[J]. Sensors, 2014, 14(2):2595-2618.