面向病人睡眠呼吸信号的非接触式测量与提取
|
摘要
睡眠中的呼吸信号可以反映出人体的许多重要信息,在病人监护和睡眠质量评估方面有着重要的应用。为降低呼吸信号采集对病人正常休息的影响,本文设计实现了一种对于睡眠呼吸信号的非接触式提取与检测方法。本方法采用非接触式测量方式,通过测量床垫中传导的呼吸振动信号,对呼吸信号进行测量。由于采集得到的信号具有衰减大、噪声高的特点,需要进行处理。为此,本文基于小波分解与去噪的原理,通过串联多次小波变换实现对于睡眠时呼吸信号的去噪与提取。仿真与实验表明,本方法提取效果较好,提取结果可以正确反映病人的呼吸频率等生理指标。
The sleeping respiratory signal can reflect a lot of important information of human health, which has important applications in patient monitoring and the sleeping quality evaluation. In this paper, to reduce the negative effects of the signal acquisition to the patients, a non-connect extraction and measurement method of the sleeping respiratory signal is put forward. In this method, the sleeping respiratory signal is acquired via a non-connected method by measuring the respiratory signal conducted in the mattress. Since the acquired signal has large attenuation and high noise, a signal denoising and extraction method based on wavelet analysis algorithm is put forward. Through the cascade of multiple levels of wavelet analysis, the sleeping respiratory signal is denoised and extracted. Examples on the signals of author points out that this method can extract the sleeping respiratory signal correctly, the extraction results can reflect the physiological indexes such as the respiratory frequency.
The sleeping respiratory signal can reflect a lot of important information of human health, which has important applications in patient monitoring and the sleeping quality evaluation. In this paper, to reduce the negative effects of the signal acquisition to the patients, a non-connect extraction and measurement method of the sleeping respiratory signal is put forward. In this method, the sleeping respiratory signal is acquired via a non-connected method by measuring the respiratory signal conducted in the mattress. Since the acquired signal has large attenuation and high noise, a signal denoising and extraction method based on wavelet analysis algorithm is put forward. Through the cascade of multiple levels of wavelet analysis, the sleeping respiratory signal is denoised and extracted. Examples on the signals of author points out that this method can extract the sleeping respiratory signal correctly, the extraction results can reflect the physiological indexes such as the respiratory frequency.
引文
[1]郭永丛,司玉娟.基于心电信号提取呼吸信号的算法[J].吉林大学学报(信息科学版),2016,34(3):327-333.
[2]祁富贵,李川涛,张华等.一种非接触呼吸暂停检测技术的研究[J].中国医疗设备,2014,29(12):24-27,35.
[3]张淑清,上官寒露,袁计委等.基于内禀模态能量比呼吸信号特征参数提取方法[J].仪器仪表学报,2010,31(8):1706-1711.
[4]储泰山,陆美珠,马志新.基于床垫式生命监测仪的呼吸率检测[J].科技创新与应用,2014,(18):5-6.
[5]单禹皓,陈通,温万惠等.呼吸信号的非接触式测量[J].计算机科学,2015,42(10):43-44,75.
[6]张华,荆钧尧,李盛等.模拟家庭监护的非接触呼吸和心跳信号分离实验研究[J].电子科技,2013,26(9):57-60.
[7]戴鹏,曹忠丽,陈飞鸣等.一种非接触式人体生理信号监测方法[J].传感器与微系统,2013,32(3):49-52.
[8]杨子凯,王建林,于涛等.基于预测误差法的加速度传感器动态模型参数辨识[J].仪器仪表学报,2015,36(6):1244-1249.
[9]林开司,张露,林开武.巴特沃斯低通滤波器优化设计与仿真研究[J].重庆工商大学学报(自然科学版),2014,31(6):58-62.
[10]马豪,尹健龙,李东升.基于小波分解的磨床声发射特征信号提取方法[J].传感器与微系统,2016,35(7):13-15.
[11]张爱华,王平,丑永新.基于动态差分阈值的脉搏信号峰值检测算法[J].吉林大学学报(工学版),2014,44(3):847-853.
[12]胡锡坤,金添.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[J].雷达学报,2016,5(5):462-469.
[13]李醒飞,纪越,吴军.高精度超声测距系统中自相关小波去噪法[J].纳米技术与精密工程,2016,14(3):179-185.
[14]李红延,周云龙,田峰等.一种新的小波自适应阈值函数振动信号去噪算法[J].仪器仪表学报,2015,36(10):2200-2206.
[15]吴志成,王重阳,任爱君.消除信号趋势项时小波基优选方法研究[J].北京理工大学学报,2013,33(8):811-814.
[2]祁富贵,李川涛,张华等.一种非接触呼吸暂停检测技术的研究[J].中国医疗设备,2014,29(12):24-27,35.
[3]张淑清,上官寒露,袁计委等.基于内禀模态能量比呼吸信号特征参数提取方法[J].仪器仪表学报,2010,31(8):1706-1711.
[4]储泰山,陆美珠,马志新.基于床垫式生命监测仪的呼吸率检测[J].科技创新与应用,2014,(18):5-6.
[5]单禹皓,陈通,温万惠等.呼吸信号的非接触式测量[J].计算机科学,2015,42(10):43-44,75.
[6]张华,荆钧尧,李盛等.模拟家庭监护的非接触呼吸和心跳信号分离实验研究[J].电子科技,2013,26(9):57-60.
[7]戴鹏,曹忠丽,陈飞鸣等.一种非接触式人体生理信号监测方法[J].传感器与微系统,2013,32(3):49-52.
[8]杨子凯,王建林,于涛等.基于预测误差法的加速度传感器动态模型参数辨识[J].仪器仪表学报,2015,36(6):1244-1249.
[9]林开司,张露,林开武.巴特沃斯低通滤波器优化设计与仿真研究[J].重庆工商大学学报(自然科学版),2014,31(6):58-62.
[10]马豪,尹健龙,李东升.基于小波分解的磨床声发射特征信号提取方法[J].传感器与微系统,2016,35(7):13-15.
[11]张爱华,王平,丑永新.基于动态差分阈值的脉搏信号峰值检测算法[J].吉林大学学报(工学版),2014,44(3):847-853.
[12]胡锡坤,金添.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[J].雷达学报,2016,5(5):462-469.
[13]李醒飞,纪越,吴军.高精度超声测距系统中自相关小波去噪法[J].纳米技术与精密工程,2016,14(3):179-185.
[14]李红延,周云龙,田峰等.一种新的小波自适应阈值函数振动信号去噪算法[J].仪器仪表学报,2015,36(10):2200-2206.
[15]吴志成,王重阳,任爱君.消除信号趋势项时小波基优选方法研究[J].北京理工大学学报,2013,33(8):811-814.