基于脉搏波的无创动脉硬化检测研究
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摘要
当今社会,伴随着经济的高速发展和人们生活环境、生活方式、饮食结构不断的改变,以动脉硬化为代表的心血管疾病的发病率和死亡率日趋增加。脉搏信号作为人体的一种生理信号,它所呈现出来的形态、强度、速率等能反映出人体心血管系统的许多生理病理信息,因此研究脉搏信号对于动脉硬化疾病的早期防治具有重要的意义。
本文首先介绍了脉搏波产生的机理及其中所包含的信息,分析了国内外脉搏检测及处理方法的研究现状。针对脉搏信号的特点,设计了一套脉搏信号采集装置,并运用该系统临床采集了脉搏数据,这为研究脉搏信号提供了源数据。
由于采集的脉搏信号包含有噪声和基线漂移,本文采用小波阈值法去除噪声和三次样条插值法抑制基线漂移,并取得了很好的效果。然后基于前人的研究成果和临床试验结果,选择了与动脉硬化相关的六个特征,这些特征涵盖时域和时频域,可以全面的反映脉搏波形的特征。针对提取的特征,用神经网络进行分类,识别准确率达到很高,可以很好的区分出健康人和动脉硬化患者的脉搏波形。
在对单个波形分析之后,本文还对脉搏波速进行了研究分析,主要研究了指-桡脉搏波速度。在波速分析上,首先采用传统的峰峰值法进行计算,实验结果表明指-桡脉搏波速在不同的人群中表现出不同的数值,具有一定临床参考价值。然后针对峰值受到噪声影响时出现误差的情况,提出了一种新的方法,即广义互相关法进行脉搏波速的计算。实验结果表明,在波峰受到干扰时,这种新方法表现出良好的性能。
最后,本文开发了一套脉搏信号检测系统,并将提出的脉象特征识别和脉搏波速计算算法应用于脉搏信号检测系统中,可以方便用户直观的了解自己的身体状况。
In the modern society, with the rapid development of the economy and the constant change of the lifestyle, environment and diet, the vascular disease (such as atherosclerosis) morbidity and mortality is on the increase. As a physiological signal of the body, the pulse signal reflects much information of the human cardiovascular according to its shape, strength and speed. So the study of the pulse signal is important for the early prevention of the atherosclerotic disease.
First, the generation mechanism and the information contained of pulse wave is introduced, along with the analysis of the detection and processing methods of the pulse wave at home and abroad. Based on the feature of the signal, a pulse signal acquisition system is designed which is applied in the clinical use to collect the pulse data.
Due to the noise contained in the collected pulse signal and the baseline drift, the wavelet threshold denoising and cubic spline interpolation are applied in this paper to suppress the baseline drift. Then, based on the previous research and clinical trial results, six features relevant to the atherosclerosis are selected, which cover the time and frequency domain and fully reflect the characteristics of the pulse waveform. For these features, the neural network is used for classification, which achieves a high accurate recognition rate and the pulse waves of people in good health and with atherosclerosis are well distinguished.
After the single waveform analysis, pulse velocity is investigated in this paper, mainly focusing on the radial pulse velocity. In the pulse velocity analysis, the traditional peak-peak method is first applied, and the results of which indicate that the radial pulse wave velocities in different population show different values and possess some clinical meaning. Then the noise interference with the peak considered, a new method called generalized cross correlation method is proposed to calculate the pulse velocity. The results show that when the peak is contaminated by the interference, the new method achieves a better performance.
Finally, a set of pulse signal detection system is developed in this paper, in which the pulse features recognition and the pulse velocity calculation method proposed are applied. The system is convenient for the users to observe their physical condition.
本文首先介绍了脉搏波产生的机理及其中所包含的信息,分析了国内外脉搏检测及处理方法的研究现状。针对脉搏信号的特点,设计了一套脉搏信号采集装置,并运用该系统临床采集了脉搏数据,这为研究脉搏信号提供了源数据。
由于采集的脉搏信号包含有噪声和基线漂移,本文采用小波阈值法去除噪声和三次样条插值法抑制基线漂移,并取得了很好的效果。然后基于前人的研究成果和临床试验结果,选择了与动脉硬化相关的六个特征,这些特征涵盖时域和时频域,可以全面的反映脉搏波形的特征。针对提取的特征,用神经网络进行分类,识别准确率达到很高,可以很好的区分出健康人和动脉硬化患者的脉搏波形。
在对单个波形分析之后,本文还对脉搏波速进行了研究分析,主要研究了指-桡脉搏波速度。在波速分析上,首先采用传统的峰峰值法进行计算,实验结果表明指-桡脉搏波速在不同的人群中表现出不同的数值,具有一定临床参考价值。然后针对峰值受到噪声影响时出现误差的情况,提出了一种新的方法,即广义互相关法进行脉搏波速的计算。实验结果表明,在波峰受到干扰时,这种新方法表现出良好的性能。
最后,本文开发了一套脉搏信号检测系统,并将提出的脉象特征识别和脉搏波速计算算法应用于脉搏信号检测系统中,可以方便用户直观的了解自己的身体状况。
In the modern society, with the rapid development of the economy and the constant change of the lifestyle, environment and diet, the vascular disease (such as atherosclerosis) morbidity and mortality is on the increase. As a physiological signal of the body, the pulse signal reflects much information of the human cardiovascular according to its shape, strength and speed. So the study of the pulse signal is important for the early prevention of the atherosclerotic disease.
First, the generation mechanism and the information contained of pulse wave is introduced, along with the analysis of the detection and processing methods of the pulse wave at home and abroad. Based on the feature of the signal, a pulse signal acquisition system is designed which is applied in the clinical use to collect the pulse data.
Due to the noise contained in the collected pulse signal and the baseline drift, the wavelet threshold denoising and cubic spline interpolation are applied in this paper to suppress the baseline drift. Then, based on the previous research and clinical trial results, six features relevant to the atherosclerosis are selected, which cover the time and frequency domain and fully reflect the characteristics of the pulse waveform. For these features, the neural network is used for classification, which achieves a high accurate recognition rate and the pulse waves of people in good health and with atherosclerosis are well distinguished.
After the single waveform analysis, pulse velocity is investigated in this paper, mainly focusing on the radial pulse velocity. In the pulse velocity analysis, the traditional peak-peak method is first applied, and the results of which indicate that the radial pulse wave velocities in different population show different values and possess some clinical meaning. Then the noise interference with the peak considered, a new method called generalized cross correlation method is proposed to calculate the pulse velocity. The results show that when the peak is contaminated by the interference, the new method achieves a better performance.
Finally, a set of pulse signal detection system is developed in this paper, in which the pulse features recognition and the pulse velocity calculation method proposed are applied. The system is convenient for the users to observe their physical condition.
引文
[1]Lusis A J. Atherosclerosis [J]. Nature,2000,407(6801):233-241.
[2]Ghazanfar Q, Richard B, Louis S. Relationship between aortic atherosclerosis and non-invasive measures of arterial stiffness [J]. Nature,2007,195(2):190-193.
[3]Hsien-Tsai Wu. A Bio Signal Processing System for Pulse Wave Velocity Detection[C]. 9th International Conference on Engineering Education, july23-28,2006.
[4]杨丽兰,张水望,李天亮等.高血压病人肱动脉内皮依赖性舒张功能临床研究[J].中西医结合心脑血管病杂志,2005,3(2)：114-116.
[5]Taddei S, Virdis A, Mattei P, et al. Aging and endothelial function innormotensive subjects and patients with essential hypertension [J]. Circulation,1995,91 (3):1981-1987.
[6]岳沛平.脉象与脉图在心血管疾病临床诊断中的应用.中医文献杂志,2006,1：31-33
[7]Flaherty et al. Rapid non-invasive blood pressure measuring device. Unite States Patent. February,2000-6027452
[8]Yunquan Chen. Continuous non-invasive blood pressure monitoring method and apparatus. Unite States Patent. July,2003-6599251
[9]Talke PO. Measurement of systolic blood pressure using pulse oximetry during helicopter flight[J]. Crit Care Med,1991,19:934-937.
[10]张东青,王元昔.新型脉搏波传感器的研制[J].传感器技术,2001,20(5)：46-49.
[11]朱涛,王妙基,伍珠美.脉搏图法心输出量测定与热稀释法的对比[J].临床麻醉学杂志,2005,21(1)：50-51.
[12]Cecchetti C, Stoppa F, Vanacore N, et al. Monitoring of intrathoracic volemia and cardiac output in criticallyi ill children[J].Minerva Anestesiologica,2003, 69(12):907-918.
[13]陈春晓,刘建业,王惠南.无创心血管功能测试诊断仪的研究[J].生物医学工学杂志,2003,20(1)：125-127.
[14]Hsien-Tsai Wu, Cheng-Tso Yao, Tsang-Chih Wu. Development of Easy Operating Arterial Stiffness Assessment Instrument for Homecare [C]. Proceedings of the 29th Annual International Conference of the IEEE EMBS, August 23-26,2007, pp5868-5871.
[15]郑文,李峰等.心血管功能脉图检测诊断系统的设计和实现[J].计算机工程与应用,40(22)：199-201.
[16]Huiyan Wang, Peiyong Zhang. Investigation on the automatic parameters extraction of pulse signals based on wavelet transform[J]. Journal of Zhejiang University Science,2007,8(8):1283-1289.
[17]罗志昌,张松,杨益民.脉搏波的工程分析与临床应用[M],北京：科学出版社,2006.
[18]王炳和,罗建,相敬林.人体脉搏功率谱分析与中医脉诊机理研究[J].西北大学学报(自然科学版),2001,31(1),21-23.
[19]张镜人,杨天权,郑秀春等.脉象图频域指标和时域指的典型相关分析[J].辽宁中医杂志,1995,22(9),393-395.
[20]Lee C T, Wei L Y. Frequency distribution of human pulse spectra [J]. IEEE Transaetions on Biomedical Engineering,1985,32(3):245-247.
[21]Chen Liang, Wei Yu, Zhang Tao. Instantaneous frequency estimation of the sequence of hopping-frequency based on short-time Fourier transform [J]. Information Technology,2006,11:48-50.
[22]张丽琼,王炳和.基于小波变换的脉象信号特征提取方法[J].数据采集与处理,2004,19(3)：323-325.
[23]Cuiwei Li, Chongzun Zheng, Changfeng Tai. Detection of ECG characteristic points using wavelet transforms[J]. IEEE Transaetions on Biomedical Engineering, 1995,42(1):21.
[24]陈荣山,陈东汉.模糊数学中医脉象模式识别中应用[J].医学信息(云南),1998,11(3)：20-21.
[25]王洁贞,束怀符,肖珙.几种常见中医脉象的计量诊断方法[J].山东大学学报(医学版)1983,4：22-35
[26]Jadhav UM, Kadam NN. Non-invasive assessment of arterial stiffness by pulse wave velocity correlates with endothelial dysfunction[J]. Indian Heart Journal,2005,57:226-232.
[27]Yung-Kang Chen, Hsien-Tsai Wu, Ming-Chun Wang.A new dual channel pulse wave velocity measurement system[C]. Fourth IEEE Symposium on BIBE, May2004, pp.17-21.
[28]D. S. Charles, M. D. Fox. Pulse Wave Velocity for Cardiovascular characterization[C]. Proceedings of the IEEE,23rd Northeast on Bioengineering, May 1997, pp77-78.
[29]贺振泉,原林.从经络筋膜学说看脉象形成机理[J].中国中医基础医学杂志,2005,11(7)：482-492.
[30]元慧.脉搏波的特征信息分析和动脉硬化诊断研究[D].济南：山东大学,2005,
[31]季忠,秦树人著.微弱生物医学信号特征提取的原理与实现[M].北京：科学出版社,2007,
[32]杨福生.小波变换的工程分析和应用[G].北京：科学出版社,1999.
[33]Donoho D. L. De-noising by Soft-thresholding[J]. IEEE Trans on Information Theory. 1995,41(3):613-627.
[34]Chang S G, Yu B, Vetterli M. Adaptive wavelet thresholding for image denoising and compression[J]. IEEE TransImage Processing,2000,9(9):1532-1546.
[35]李艳文,姜印平,郑彤.基于小波变换的脉搏波信号去噪[J].河北工业大学报.2005,8：82-85.
[36]De Meemman R E, Zion AS, Teitelbaum S. Deriving Respiration from Pulse Wave:A New Signal-Processing Technique[J]. American Journal of Physiology,1996,39(5): H1672-H1675.
[37]夏恒超,詹永麒.一种新的基于三次样条插值技术的心电图基线漂移消除方法[J].生物医学工程学杂志,2003,20(3)：524-526.
[38]罗志昌,张松,杨文鸣.脉搏波波形特征信息的研究[J].北京工业大学报报.1996,22(1)：293-295.
[39]Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc R Soc, 1998, A454:903-995.
[40]李永勤,王清,邓亲恺.EMD及其在生物医学信号处理中的应用研究[J].生物医学工程学杂志,2005,22(05)：1058-1062.
[41]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京：清华大学出版社,2005.
[42]Alty S R, Natalta A J, Millasseau S C. Predicting arterial stiffness from the digital volume pulse waveform [J]. IEEE Transactions on Biomedica Engineering,2007, 54(12):2268-2271.
[43]王宏禹,邱天爽.自适应噪声抵消与时间延迟估计[M].大连：大连理工大学出版社,1999.
[2]Ghazanfar Q, Richard B, Louis S. Relationship between aortic atherosclerosis and non-invasive measures of arterial stiffness [J]. Nature,2007,195(2):190-193.
[3]Hsien-Tsai Wu. A Bio Signal Processing System for Pulse Wave Velocity Detection[C]. 9th International Conference on Engineering Education, july23-28,2006.
[4]杨丽兰,张水望,李天亮等.高血压病人肱动脉内皮依赖性舒张功能临床研究[J].中西医结合心脑血管病杂志,2005,3(2)：114-116.
[5]Taddei S, Virdis A, Mattei P, et al. Aging and endothelial function innormotensive subjects and patients with essential hypertension [J]. Circulation,1995,91 (3):1981-1987.
[6]岳沛平.脉象与脉图在心血管疾病临床诊断中的应用.中医文献杂志,2006,1：31-33
[7]Flaherty et al. Rapid non-invasive blood pressure measuring device. Unite States Patent. February,2000-6027452
[8]Yunquan Chen. Continuous non-invasive blood pressure monitoring method and apparatus. Unite States Patent. July,2003-6599251
[9]Talke PO. Measurement of systolic blood pressure using pulse oximetry during helicopter flight[J]. Crit Care Med,1991,19:934-937.
[10]张东青,王元昔.新型脉搏波传感器的研制[J].传感器技术,2001,20(5)：46-49.
[11]朱涛,王妙基,伍珠美.脉搏图法心输出量测定与热稀释法的对比[J].临床麻醉学杂志,2005,21(1)：50-51.
[12]Cecchetti C, Stoppa F, Vanacore N, et al. Monitoring of intrathoracic volemia and cardiac output in criticallyi ill children[J].Minerva Anestesiologica,2003, 69(12):907-918.
[13]陈春晓,刘建业,王惠南.无创心血管功能测试诊断仪的研究[J].生物医学工学杂志,2003,20(1)：125-127.
[14]Hsien-Tsai Wu, Cheng-Tso Yao, Tsang-Chih Wu. Development of Easy Operating Arterial Stiffness Assessment Instrument for Homecare [C]. Proceedings of the 29th Annual International Conference of the IEEE EMBS, August 23-26,2007, pp5868-5871.
[15]郑文,李峰等.心血管功能脉图检测诊断系统的设计和实现[J].计算机工程与应用,40(22)：199-201.
[16]Huiyan Wang, Peiyong Zhang. Investigation on the automatic parameters extraction of pulse signals based on wavelet transform[J]. Journal of Zhejiang University Science,2007,8(8):1283-1289.
[17]罗志昌,张松,杨益民.脉搏波的工程分析与临床应用[M],北京：科学出版社,2006.
[18]王炳和,罗建,相敬林.人体脉搏功率谱分析与中医脉诊机理研究[J].西北大学学报(自然科学版),2001,31(1),21-23.
[19]张镜人,杨天权,郑秀春等.脉象图频域指标和时域指的典型相关分析[J].辽宁中医杂志,1995,22(9),393-395.
[20]Lee C T, Wei L Y. Frequency distribution of human pulse spectra [J]. IEEE Transaetions on Biomedical Engineering,1985,32(3):245-247.
[21]Chen Liang, Wei Yu, Zhang Tao. Instantaneous frequency estimation of the sequence of hopping-frequency based on short-time Fourier transform [J]. Information Technology,2006,11:48-50.
[22]张丽琼,王炳和.基于小波变换的脉象信号特征提取方法[J].数据采集与处理,2004,19(3)：323-325.
[23]Cuiwei Li, Chongzun Zheng, Changfeng Tai. Detection of ECG characteristic points using wavelet transforms[J]. IEEE Transaetions on Biomedical Engineering, 1995,42(1):21.
[24]陈荣山,陈东汉.模糊数学中医脉象模式识别中应用[J].医学信息(云南),1998,11(3)：20-21.
[25]王洁贞,束怀符,肖珙.几种常见中医脉象的计量诊断方法[J].山东大学学报(医学版)1983,4：22-35
[26]Jadhav UM, Kadam NN. Non-invasive assessment of arterial stiffness by pulse wave velocity correlates with endothelial dysfunction[J]. Indian Heart Journal,2005,57:226-232.
[27]Yung-Kang Chen, Hsien-Tsai Wu, Ming-Chun Wang.A new dual channel pulse wave velocity measurement system[C]. Fourth IEEE Symposium on BIBE, May2004, pp.17-21.
[28]D. S. Charles, M. D. Fox. Pulse Wave Velocity for Cardiovascular characterization[C]. Proceedings of the IEEE,23rd Northeast on Bioengineering, May 1997, pp77-78.
[29]贺振泉,原林.从经络筋膜学说看脉象形成机理[J].中国中医基础医学杂志,2005,11(7)：482-492.
[30]元慧.脉搏波的特征信息分析和动脉硬化诊断研究[D].济南：山东大学,2005,
[31]季忠,秦树人著.微弱生物医学信号特征提取的原理与实现[M].北京：科学出版社,2007,
[32]杨福生.小波变换的工程分析和应用[G].北京：科学出版社,1999.
[33]Donoho D. L. De-noising by Soft-thresholding[J]. IEEE Trans on Information Theory. 1995,41(3):613-627.
[34]Chang S G, Yu B, Vetterli M. Adaptive wavelet thresholding for image denoising and compression[J]. IEEE TransImage Processing,2000,9(9):1532-1546.
[35]李艳文,姜印平,郑彤.基于小波变换的脉搏波信号去噪[J].河北工业大学报.2005,8：82-85.
[36]De Meemman R E, Zion AS, Teitelbaum S. Deriving Respiration from Pulse Wave:A New Signal-Processing Technique[J]. American Journal of Physiology,1996,39(5): H1672-H1675.
[37]夏恒超,詹永麒.一种新的基于三次样条插值技术的心电图基线漂移消除方法[J].生物医学工程学杂志,2003,20(3)：524-526.
[38]罗志昌,张松,杨文鸣.脉搏波波形特征信息的研究[J].北京工业大学报报.1996,22(1)：293-295.
[39]Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proc R Soc, 1998, A454:903-995.
[40]李永勤,王清,邓亲恺.EMD及其在生物医学信号处理中的应用研究[J].生物医学工程学杂志,2005,22(05)：1058-1062.
[41]周开利,康耀红.神经网络模型及其MATLAB仿真程序设计[M].北京：清华大学出版社,2005.
[42]Alty S R, Natalta A J, Millasseau S C. Predicting arterial stiffness from the digital volume pulse waveform [J]. IEEE Transactions on Biomedica Engineering,2007, 54(12):2268-2271.
[43]王宏禹,邱天爽.自适应噪声抵消与时间延迟估计[M].大连：大连理工大学出版社,1999.
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