强噪声背景下的脉搏血氧饱和度检测
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摘要
氧气供给是否正常关系到人体能否进行正常的新陈代谢,而血氧饱和度是反映供氧状态的一个重要指标。脉搏血氧饱和度检测仪可以实现血氧饱和度的无创检测,在临床和家庭保健中广泛使用。本论文针对这类检测仪器目前存在的模拟电路复杂,稳定性差,运动伪差干扰难以去除的缺陷,提出主要采用软件方法实现脉搏波信号的预处理和特征参数的提取,综合引入形态学方法,平移不变量提升小波阂值法和经验模态分解方法实现了强噪声背景下脉搏波信号的提取,提高了血氧饱和度检测的准确性。论文主要包含了以下几个方面：
(1)设计并完成了以ATmega8单片机作为核心的双波长指端透射光硬件检测电路。这款单片机I/O口可以输出、吸收20mA的电流,能够直接驱动双波长LED,同时其内部集成的10-bit ADC也使模拟电路的设计大为简化。
(2)提出将信号传送到上位机用软件方法实现强噪声干扰下脉搏波信号的提取。首次采用形态学方法和平移不变量提升小波阈值法级联的综合滤波算法实现脉搏波信号的基线矫正和高频干扰去除。综合方法将形态学滤波器在滤除基线漂移方面运算量小,速度快的优点和平移不变量提升小波阈值去噪法优良的高频消噪性能结合起来,在低信噪比环境下有效提取了脉搏波信号。
(3)采用经验模态分解方法在很大程度上去除了脉搏血氧饱和度检测中难以去除的运动伪差,矫正了运动伪差导致的波形畸变。用提升小波模极大值法代替传统的差分法提取预处理后脉搏波信号的特征点,即使在含有噪声干扰的情况下也能实现特征点的准确提取,并且提升小波的引入使得传统的模极大值法分解更为简单快速。
(4)利用血氧饱和度的定义和提取出的脉搏波信号的特征参数计算出脉搏血氧饱和度,实现了对人体脉搏血氧饱和度的实时测量和连续监测。
论文最后对全文的工作和存在的不足进行了总结,并对下一步的研究工作进行了展望。
The oxygen supply affects the metabolism in human body, and oxygen saturation is an important index of evaluating oxygen supply condition. Transmission pulse oximeter has been used widely in clinical and family health care which can realize non-invasive oxygen saturation detection. Traditional oximeter applies complex analogue electric circuits to realize signal processing, this leading to poor stability. Moreover, it is hard to eliminate the motion artifact. To solve these problems, this thesis proposes using software method to process pulse signal and extract its characteristic parameters. By implying morphology method, translation invariant lifting wavelet method, and empirical mode decomposition method, weak pulse signal submerged in noise is extracted. This improves detection accuracy. The main content on the paper is summarized as follows:
Firstly, the hardware platform is designed and completed to detect dual-wavelength finger transmitted light. This platform is based on the ATmega8 MCU. The I/O ports of this MCU have nearly 20mA current driving capability which can activate the dual-wavelength light source directly. The internal integrated 10-bit analog-to-digital converter (ADC) in MCU also simplifies the circuit design.
Secondly, we transmit the signal to computer in order to process pulse signal with software method. This paper combines the morphological filter with the translation invariant lifting wavelet method to remove baseline drift and high frequency noise in pulse wave. The comprehensive method combines their good merits. Morphological filter has small computational work and a high processing speed. Its performance in removing baseline drift is nearly perfect. Translation invariant lifting wavelet method has excellent performance in high-frequency noise removing. Comprehensive method can extract pulse wave signals excellently when the signal-to-noise ratio is quite low.
Thirdly, the empirical mode decomposition method is used to eliminate the motion artifact and correct waveform distortion. Then the lifting wavelet modulus maxima method is used to realize feature point detection of the pretreated pulse signal. Compared to the traditional wavelet modulus maxima method, it achieves similar examination precision with less computation and high efficiency.
Finally, pulse oxygen saturation is calculated on the basis of its definition and pulse signal's characteristic parameters. The result shows this system has achieved real time pulse oxygen saturation measurement and continuous monitoring of human body.
A summation is made to generalize the work and deficiency in this paper. Suggestions are proposed for the further research and improvements.
(1)设计并完成了以ATmega8单片机作为核心的双波长指端透射光硬件检测电路。这款单片机I/O口可以输出、吸收20mA的电流,能够直接驱动双波长LED,同时其内部集成的10-bit ADC也使模拟电路的设计大为简化。
(2)提出将信号传送到上位机用软件方法实现强噪声干扰下脉搏波信号的提取。首次采用形态学方法和平移不变量提升小波阈值法级联的综合滤波算法实现脉搏波信号的基线矫正和高频干扰去除。综合方法将形态学滤波器在滤除基线漂移方面运算量小,速度快的优点和平移不变量提升小波阈值去噪法优良的高频消噪性能结合起来,在低信噪比环境下有效提取了脉搏波信号。
(3)采用经验模态分解方法在很大程度上去除了脉搏血氧饱和度检测中难以去除的运动伪差,矫正了运动伪差导致的波形畸变。用提升小波模极大值法代替传统的差分法提取预处理后脉搏波信号的特征点,即使在含有噪声干扰的情况下也能实现特征点的准确提取,并且提升小波的引入使得传统的模极大值法分解更为简单快速。
(4)利用血氧饱和度的定义和提取出的脉搏波信号的特征参数计算出脉搏血氧饱和度,实现了对人体脉搏血氧饱和度的实时测量和连续监测。
论文最后对全文的工作和存在的不足进行了总结,并对下一步的研究工作进行了展望。
The oxygen supply affects the metabolism in human body, and oxygen saturation is an important index of evaluating oxygen supply condition. Transmission pulse oximeter has been used widely in clinical and family health care which can realize non-invasive oxygen saturation detection. Traditional oximeter applies complex analogue electric circuits to realize signal processing, this leading to poor stability. Moreover, it is hard to eliminate the motion artifact. To solve these problems, this thesis proposes using software method to process pulse signal and extract its characteristic parameters. By implying morphology method, translation invariant lifting wavelet method, and empirical mode decomposition method, weak pulse signal submerged in noise is extracted. This improves detection accuracy. The main content on the paper is summarized as follows:
Firstly, the hardware platform is designed and completed to detect dual-wavelength finger transmitted light. This platform is based on the ATmega8 MCU. The I/O ports of this MCU have nearly 20mA current driving capability which can activate the dual-wavelength light source directly. The internal integrated 10-bit analog-to-digital converter (ADC) in MCU also simplifies the circuit design.
Secondly, we transmit the signal to computer in order to process pulse signal with software method. This paper combines the morphological filter with the translation invariant lifting wavelet method to remove baseline drift and high frequency noise in pulse wave. The comprehensive method combines their good merits. Morphological filter has small computational work and a high processing speed. Its performance in removing baseline drift is nearly perfect. Translation invariant lifting wavelet method has excellent performance in high-frequency noise removing. Comprehensive method can extract pulse wave signals excellently when the signal-to-noise ratio is quite low.
Thirdly, the empirical mode decomposition method is used to eliminate the motion artifact and correct waveform distortion. Then the lifting wavelet modulus maxima method is used to realize feature point detection of the pretreated pulse signal. Compared to the traditional wavelet modulus maxima method, it achieves similar examination precision with less computation and high efficiency.
Finally, pulse oxygen saturation is calculated on the basis of its definition and pulse signal's characteristic parameters. The result shows this system has achieved real time pulse oxygen saturation measurement and continuous monitoring of human body.
A summation is made to generalize the work and deficiency in this paper. Suggestions are proposed for the further research and improvements.
引文
[1]高咏芬,杨丽华.无创脉搏氧饱和度监测仪的临床应用[J].护理研究,2003,12(8)：938-939.
[2]周颖,王珊娟,杭燕南.脉搏血氧饱和度监测的进展及其正确性评价[EB/OL]. (2007-8-15) [2011-03-06]. http://www.labbase.net/News/ShowNews Details-1-18-DA3574BE1CC78E30.html.
[3]徐雅洁.基于DSP脉搏血氧测量平台的设计及实现[D].天津：天津大学精密仪器与光电子工程学院,2009.
[4]闻大祥,杭燕南.脉搏氧饱和度监测的新技术一MasiITlo信号萃取技术[J].中国医学装备,2004,1(2)：20-21.
[5]张虹,金捷,孙卫新.数字式脉搏血氧饱和度检测系统的研制[J].生物医学工程与临床,2002,6(3)：126-127.
[6]游荐波.基于C8051F021单片机的脉搏血氧饱和度测量仪的研制[D].长沙：国防科技大学电子科学与工程学院,2008.
[7]张虹,金捷,孙卫新.脉搏血氧仪中光电容积脉搏波的软件检出方法[J].第四军医大学学报,2001,22(1)：94-96.
[8]贾佳,马春.基于MSP430F149和nRF905的无线血氧指夹的设计[EB/OL]. (2010-5-22) [2011-03-06]. http://arm.cepark.com/index.php/action-viewnews-itemid-199.
[9]张彬.基于脉搏波的血氧饱和度检测算法的研究[D].北京：北京交通大学计算机与信息技术学院,2009.
[10]潘迎春.脉搏血氧饱和度监测在脑外科的应用[J/OL].实用护理学杂志,2008,(145):1 [2011-03-06]. http://www.lc1995.com/mn95_02/searchshow.asp?id= 1443.
[11]严新忠,杨静,郭略.人体血氧饱和度监测方法的研究[J].医疗卫生装备,2005,10(8)：2-5.
[12]潘霞莲.基于LabVIEW的无线血氧饱和度监测系统设计[D].江苏：江苏大学电子信息工程学院,2010.
[13]江丽仪,林绍杰,吴效明.基于ZigBee的无线血氧检测模块研究[A].中国仪器仪表学会医疗仪器分会第四次全国会员代表大会暨2009年学术年会论文集[C].慈溪,2010：186-187.
[14]龚涵,陈浩宇.微弱光电信号检测电路的设计与实现[J].科技信息,2007,8：85-86.
[15]苏永春,于魏,姚翔.利用LMS算法处理血氧饱和度监测中的脉搏波信号[J].医疗卫生装备,2006,27(7)：12-13.
[16]Ma Heng, Xu Jiangning, Zhu Tao. A new wavelet de-nosing algorithm based on reversible transform[A]. Proceedings of the 3rd Conference on Sensing Technology[C]. Wuhan,2008:265-268.
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[18]杨琳,张松,杨益民,等.利用三次样条差值法抑制脉搏波基线漂移[J].北京生物医学工程,2010,29(2)：199-200.
[19]王鹏,魏守水,黄青华.基于小波变换得自适应滤波器消除脉搏波基线漂移[J].中国医学物理学杂志,2004,21(5)：296-298.
[20]赵志华,穆海军.基于组合形态滤波与自适应广义形态滤波的ECG去噪算法[J].科学技术与工程,2010,10(7)：1681-1682.
[21]Ji TY, Lu Z, Wu QH, et al. Baseline normalization of ECG signals using empirical mode decomposition and mathematical morphology[J]. Electronics Letters,2008,44(2):82-83.
[22]祝宇红,张富强,李满天,等.一种基于数学形态学的脉搏波信号预处理方法研究[J].北京生物医学工程,2009,28(2)：123-125.
[23]王伟,蒋式勤.去除心磁信号基线漂移的形态学滤波器[J].技术交流,2008,24(5)：45-46.
[24]Chernogor LF, Lazorenko OV, Lazorenko SV. Wavelet analysis of the ultrawideband signals [A]. Proceedings of the 4th International Conference on Ultrawideband and Ultrashort Impulse Signals[C]. Sevastopol,2008:210-211.
[25]Wang Baoqin, Wang Gang, Fu Yuexia, et al. The Mallat algorithm for a class of orthogonal wavelet on compact Lie Groups [A]. Proceedings of International Conference on Wavelet Analysis and Pattern Recognition[C]. Baoding,2009: 413-415.
[26]Huang Tao, Qin Lele. Image denoising research based on lifting wavelet transform and threshold optimization[A]. Proceedings of the 3rd IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications[C]. Beijing,2009:1218-1219.
[27]Li Min, He Guangpu, Zhang Xiaoying, et al. Power quality detecting based on fast lifting wavelet transform[A]. Proceedings of Control and Decision Conference[C]. Yantai,2008:536-538.
[28]Salwani MD, Jasmy Y. Comparison of few wavelets to filter ocular artifacts in EEG using lifting wavelet transform[J]. IEEE,2005,21(24):1-3.
[29]Dohono DL. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory,1995,41(3):613-627.
[30]Su Li, Zhao Guoliang, Zhang Renyan. Translation-invariant wavelet de-noising method with improved thresholding [A]. Proceedings of IEEE International Symposium on Communications and Information Technology[C]. Harbin,2005: 615-619.
[31]高振斌,贾希,贾志成.基于小波变换的脑电信号降噪方法的研究[J].河北工业大学学报,2006,35(6)：32-33.
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[2]周颖,王珊娟,杭燕南.脉搏血氧饱和度监测的进展及其正确性评价[EB/OL]. (2007-8-15) [2011-03-06]. http://www.labbase.net/News/ShowNews Details-1-18-DA3574BE1CC78E30.html.
[3]徐雅洁.基于DSP脉搏血氧测量平台的设计及实现[D].天津：天津大学精密仪器与光电子工程学院,2009.
[4]闻大祥,杭燕南.脉搏氧饱和度监测的新技术一MasiITlo信号萃取技术[J].中国医学装备,2004,1(2)：20-21.
[5]张虹,金捷,孙卫新.数字式脉搏血氧饱和度检测系统的研制[J].生物医学工程与临床,2002,6(3)：126-127.
[6]游荐波.基于C8051F021单片机的脉搏血氧饱和度测量仪的研制[D].长沙：国防科技大学电子科学与工程学院,2008.
[7]张虹,金捷,孙卫新.脉搏血氧仪中光电容积脉搏波的软件检出方法[J].第四军医大学学报,2001,22(1)：94-96.
[8]贾佳,马春.基于MSP430F149和nRF905的无线血氧指夹的设计[EB/OL]. (2010-5-22) [2011-03-06]. http://arm.cepark.com/index.php/action-viewnews-itemid-199.
[9]张彬.基于脉搏波的血氧饱和度检测算法的研究[D].北京：北京交通大学计算机与信息技术学院,2009.
[10]潘迎春.脉搏血氧饱和度监测在脑外科的应用[J/OL].实用护理学杂志,2008,(145):1 [2011-03-06]. http://www.lc1995.com/mn95_02/searchshow.asp?id= 1443.
[11]严新忠,杨静,郭略.人体血氧饱和度监测方法的研究[J].医疗卫生装备,2005,10(8)：2-5.
[12]潘霞莲.基于LabVIEW的无线血氧饱和度监测系统设计[D].江苏：江苏大学电子信息工程学院,2010.
[13]江丽仪,林绍杰,吴效明.基于ZigBee的无线血氧检测模块研究[A].中国仪器仪表学会医疗仪器分会第四次全国会员代表大会暨2009年学术年会论文集[C].慈溪,2010：186-187.
[14]龚涵,陈浩宇.微弱光电信号检测电路的设计与实现[J].科技信息,2007,8：85-86.
[15]苏永春,于魏,姚翔.利用LMS算法处理血氧饱和度监测中的脉搏波信号[J].医疗卫生装备,2006,27(7)：12-13.
[16]Ma Heng, Xu Jiangning, Zhu Tao. A new wavelet de-nosing algorithm based on reversible transform[A]. Proceedings of the 3rd Conference on Sensing Technology[C]. Wuhan,2008:265-268.
[17]Luo Zhizeng, Li Yafei, Meng Ming. SEMG de-noising based on the lifting wavelet transform[A]. Proceedings of the 3rd Conference on Bioinformatics and Biomedical Engineering[C]. Beijing,2009:1-4.
[18]杨琳,张松,杨益民,等.利用三次样条差值法抑制脉搏波基线漂移[J].北京生物医学工程,2010,29(2)：199-200.
[19]王鹏,魏守水,黄青华.基于小波变换得自适应滤波器消除脉搏波基线漂移[J].中国医学物理学杂志,2004,21(5)：296-298.
[20]赵志华,穆海军.基于组合形态滤波与自适应广义形态滤波的ECG去噪算法[J].科学技术与工程,2010,10(7)：1681-1682.
[21]Ji TY, Lu Z, Wu QH, et al. Baseline normalization of ECG signals using empirical mode decomposition and mathematical morphology[J]. Electronics Letters,2008,44(2):82-83.
[22]祝宇红,张富强,李满天,等.一种基于数学形态学的脉搏波信号预处理方法研究[J].北京生物医学工程,2009,28(2)：123-125.
[23]王伟,蒋式勤.去除心磁信号基线漂移的形态学滤波器[J].技术交流,2008,24(5)：45-46.
[24]Chernogor LF, Lazorenko OV, Lazorenko SV. Wavelet analysis of the ultrawideband signals [A]. Proceedings of the 4th International Conference on Ultrawideband and Ultrashort Impulse Signals[C]. Sevastopol,2008:210-211.
[25]Wang Baoqin, Wang Gang, Fu Yuexia, et al. The Mallat algorithm for a class of orthogonal wavelet on compact Lie Groups [A]. Proceedings of International Conference on Wavelet Analysis and Pattern Recognition[C]. Baoding,2009: 413-415.
[26]Huang Tao, Qin Lele. Image denoising research based on lifting wavelet transform and threshold optimization[A]. Proceedings of the 3rd IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications[C]. Beijing,2009:1218-1219.
[27]Li Min, He Guangpu, Zhang Xiaoying, et al. Power quality detecting based on fast lifting wavelet transform[A]. Proceedings of Control and Decision Conference[C]. Yantai,2008:536-538.
[28]Salwani MD, Jasmy Y. Comparison of few wavelets to filter ocular artifacts in EEG using lifting wavelet transform[J]. IEEE,2005,21(24):1-3.
[29]Dohono DL. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory,1995,41(3):613-627.
[30]Su Li, Zhao Guoliang, Zhang Renyan. Translation-invariant wavelet de-noising method with improved thresholding [A]. Proceedings of IEEE International Symposium on Communications and Information Technology[C]. Harbin,2005: 615-619.
[31]高振斌,贾希,贾志成.基于小波变换的脑电信号降噪方法的研究[J].河北工业大学学报,2006,35(6)：32-33.
[32]李杰,曲芸,刘俊,等.模平方小波阈值在MEMS陀螺信号降噪中的应用[J].中国惯性技术学报,2008,16(2)：237-238.
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