基于复合式压力传感器脉象仪的脉象数字特征分析
摘要
在传统的医学理论中,“望、闻、问、切”四诊法是中医诊病的基本方法。其中切诊,是指医生用手指触按病人的动脉搏动,以探查脉象,从而了解病情的一种诊断方法,特别是切脉,是临床上不可缺少的基本方法。但是脉象概念本身含糊,没有明确的判别标准,临床脉诊时又掺杂医生主观因素,往往分歧较多,所以脉诊客观化具有非常重要的意义。
在脉象获取阶段,数据采集仪器是由哈尔滨工业大学研制的多点复合式压力传感器设计的三部脉象获取装置。通过这台仪器获取的脉搏波形不仅可以判断寸、关、尺定位是否准确,还可以获得脉管的粗细信息。在此阶段共采集到30例年轻学生的脉象和120例外科病人的脉象,组成了研究所用的数据库。
在数据预处理阶段,针对数据中的毛刺噪声,先经过局部去毛刺,再进行平滑滤波,只需较少次平滑既干净地去除这种高频噪声,又保留了脉搏波的边缘细节。针对单点或连续多点的奇异点噪声,修改了限幅滤波算法来检测这种新噪声类型。提出了一种统计加权表决算法来检测伪峰噪声,该算法能较好克服已有算法的缺点,充分保留脉象正常的细节。最后对跨度较大的伪峰噪声和连续奇异点噪声在整个其他无噪声波段相应跨度均值统计可靠插值点做三次样条插值,对脉搏波作了曲线修补。
在脉象特征提取阶段,针对脉象的静态时域特征提取设计了信任度加权法,这种算法根据每个周期信号在预处理阶段奇异点和伪峰的噪声程度依次改变其信任度,提取到各个周期的特征值后,根据各个周期的信任度加权求其均值作为整个脉搏波的特征值。这个算法充分考虑了每个周期信号的可靠信息,和预处理阶段的历史纪录,使特征提取得更精确。为下一阶段的脉象分类提供了更好的输入数据。
在脉象分类阶段,本文改进了推广的动态时间规整算法来识别五类不同形状的脉象,针对单一模板的缺陷,每一类设计了多个模板,并根据每个周期原始信号的干净程度对分类结果施加不同程度的影响。最后尝试了引入脉象特征值匹配程度作为类别附加判定条件,试验表明改进后的算法比原来算法相比识别结果有显著的提高,其中单滑的识别率更是提高到94.67%。
In Traditional Chinese Medicine (TCM), doctors always rely on the so-called four diagnoses, including inspection, listening, inquiring and palpation to give the diagnosis. In Palpation, an essential and fundamental method, the doctor always uses fingers to feel the beating of the artery and get knowledge of the patient's illness. Due to the subjectivity and fuzziness of pulse diagnosis in TCM, quantitative systems or methods are needed to modernize pulse diagnosis.
During the data collection process, we use three multi-points pressure sensors integration systems, produced by Harbin Institute of Technology, to collect the raw pulse data. This system can synchronously detect the movements of radial artery at the positions of Cun, Guan, Chi, as well as the vascular width, using three corresponding pressure sensors. Pulse data from 30 students and 120 patients acquired by our device are carefully studied.
When we acquire the pulse data, the high frequency noise would still be introduced by the interference of electromagnetic signals. Therefore, in data preprocessing we utilize smooth filtering to eliminate noise after local deburring. A threshold filtering algorithm is introduced to detect and remove another kind of noise efficiently, and a statistics weighted approach is proposed to remove the pseudo-peaks of pulse wave, which not only overcome shortcomings of the old algorithm but preserve the details of pulse completely. Finally, it lays a solid foundation for the feature extraction and classification.
The feature extraction of pulse is important too. We make full use of the reliable information of each period by a weighted trust algorithm for the static features in time domain, extracting the features more exactly. In this algorithm, we give different trust or weight to each period of pulse wave according to its changes in preprocessing, based on which we calculate the mean value of all the weighted period as an estimation of this pulse.
In the classification phase, generalized dynamic time warping (GDTW) algorithm is utilized in this thesis to classify five kinds of pulse patterns, and a new algorithm is introduced using multiple templates and the statistics information of all the periods to handle the limitation of single template. Compared with the old algorithm, our experiment shows that our system obtains relatively reliable predictions of pulse types, and the predictive accuracy of DanHua pulse reaches as high as 94.67%.
在脉象获取阶段,数据采集仪器是由哈尔滨工业大学研制的多点复合式压力传感器设计的三部脉象获取装置。通过这台仪器获取的脉搏波形不仅可以判断寸、关、尺定位是否准确,还可以获得脉管的粗细信息。在此阶段共采集到30例年轻学生的脉象和120例外科病人的脉象,组成了研究所用的数据库。
在数据预处理阶段,针对数据中的毛刺噪声,先经过局部去毛刺,再进行平滑滤波,只需较少次平滑既干净地去除这种高频噪声,又保留了脉搏波的边缘细节。针对单点或连续多点的奇异点噪声,修改了限幅滤波算法来检测这种新噪声类型。提出了一种统计加权表决算法来检测伪峰噪声,该算法能较好克服已有算法的缺点,充分保留脉象正常的细节。最后对跨度较大的伪峰噪声和连续奇异点噪声在整个其他无噪声波段相应跨度均值统计可靠插值点做三次样条插值,对脉搏波作了曲线修补。
在脉象特征提取阶段,针对脉象的静态时域特征提取设计了信任度加权法,这种算法根据每个周期信号在预处理阶段奇异点和伪峰的噪声程度依次改变其信任度,提取到各个周期的特征值后,根据各个周期的信任度加权求其均值作为整个脉搏波的特征值。这个算法充分考虑了每个周期信号的可靠信息,和预处理阶段的历史纪录,使特征提取得更精确。为下一阶段的脉象分类提供了更好的输入数据。
在脉象分类阶段,本文改进了推广的动态时间规整算法来识别五类不同形状的脉象,针对单一模板的缺陷,每一类设计了多个模板,并根据每个周期原始信号的干净程度对分类结果施加不同程度的影响。最后尝试了引入脉象特征值匹配程度作为类别附加判定条件,试验表明改进后的算法比原来算法相比识别结果有显著的提高,其中单滑的识别率更是提高到94.67%。
In Traditional Chinese Medicine (TCM), doctors always rely on the so-called four diagnoses, including inspection, listening, inquiring and palpation to give the diagnosis. In Palpation, an essential and fundamental method, the doctor always uses fingers to feel the beating of the artery and get knowledge of the patient's illness. Due to the subjectivity and fuzziness of pulse diagnosis in TCM, quantitative systems or methods are needed to modernize pulse diagnosis.
During the data collection process, we use three multi-points pressure sensors integration systems, produced by Harbin Institute of Technology, to collect the raw pulse data. This system can synchronously detect the movements of radial artery at the positions of Cun, Guan, Chi, as well as the vascular width, using three corresponding pressure sensors. Pulse data from 30 students and 120 patients acquired by our device are carefully studied.
When we acquire the pulse data, the high frequency noise would still be introduced by the interference of electromagnetic signals. Therefore, in data preprocessing we utilize smooth filtering to eliminate noise after local deburring. A threshold filtering algorithm is introduced to detect and remove another kind of noise efficiently, and a statistics weighted approach is proposed to remove the pseudo-peaks of pulse wave, which not only overcome shortcomings of the old algorithm but preserve the details of pulse completely. Finally, it lays a solid foundation for the feature extraction and classification.
The feature extraction of pulse is important too. We make full use of the reliable information of each period by a weighted trust algorithm for the static features in time domain, extracting the features more exactly. In this algorithm, we give different trust or weight to each period of pulse wave according to its changes in preprocessing, based on which we calculate the mean value of all the weighted period as an estimation of this pulse.
In the classification phase, generalized dynamic time warping (GDTW) algorithm is utilized in this thesis to classify five kinds of pulse patterns, and a new algorithm is introduced using multiple templates and the statistics information of all the periods to handle the limitation of single template. Compared with the old algorithm, our experiment shows that our system obtains relatively reliable predictions of pulse types, and the predictive accuracy of DanHua pulse reaches as high as 94.67%.
引文
1《古今医统》内经脉候·脉诀辨要,卷四
2赵恩俭.中医脉诊学.天津科学技术出版社. 1990:1~18
3黄世林,孙明异.中医脉象研究.人民卫生出版社, 1986. 2:1~2
4李时珍[明].濒湖脉学白话解.人民卫生出版社, 1978:11~62
5王叔和[晋].脉经.人民卫生出版社, 1982:7~34
6 M. Kato, T. Koeda, et al., Measurement of digital arterial blood pressure and its recording on usual electrocardiograph paper. Tohoku J. Exp. Med., 1977,123:307~314
7 E.M.Simmons, H.Leader, S.A.Friedman, B.Davis, D.Lee, T.Winsor, C.A.Caceres. A computer program for the peripheral pulse wave. The American Journal of cardiology. 1967, 19:827~831
8 E.D. Freis and M.C. Kyle. Computer analysis of carotid and brachial pulse waves. The American Journal of Cardiology. November 1968, 22:691~695
9 S. Nisssila, M. Sorvisto. Non-invasive blood pressure measurement based on the electronic palpation method. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. HongKong, China, 1998, 20(4):1723~1726
10 B.P. Wlundell. Pulse wave patterns in patent ductus arteriosus.Archives of Disease in Childhood. 1983, 58:682~685
11傅骢远,牛欣.中医脉象今释.华夏出版社, 1993:3~51
12费兆馥.现代中医脉诊学.人民卫生出版社, 2003:15~48`
13李凌,王志中.脉搏波变异型及其心率变异性关系研究.上海交通大学博士论文. 2000: 12~14
14王炳和,相敬林.人体脉搏系统建模与脉搏信息处理方法研究.西北工业大学博士论文. 1999: 17~21
15 W.A. Lu, Y.Y. Lin Wang, W.K. Wang. Pulse analysis of patients with severs liver problems. IEEE Engineering in Medicine and Biology Magazine. Jan/Feb 1999, 18:73~75
16金伟.金氏脉学.山东科学技术出版社, 2000:3~14
17何素荣,刘世斌.临床脉图诊断学.人民军医出版社, 2003:14~61
18刘冠军.中华脉诊.中国中医药出版社, 2002:77~91
19杨天权.中医脉学应用新进展—附60则脉案分析.上海交通大学出版社, 1994:1~13
20徐迪华,徐剑秋.中医量化诊断.江苏科学技术出版社, 1997:31~75
21 S.H. Yoon, Y. Koga, I. Matsumoto, and E. Ikezono. An objective method of pulse diagnosis. American Journal of Chinese medicine. 1987, 15(3~4):147~153
22 B. Michael, and M. Michael. Instrument assisted pulse evaluation in the acupuncture practice. American Journal of Acupuncture. 1986, 14(3):255~259
23 Y.Z. Yoon, M.H. Lee and K.S. Soh. Pulse type classification by varying contact pressure. IEEE Engineering in Medicine and Biology. November/December 2000, 19:106~110
24 S. He. et al. Objectifying of pulse-taking. Journal of Japanese Eastern Medicine Society. 1977, 27(4):7
25 W.K. Wang, H.L. Chen, T.L. Hsu, et al. Alternations in human subjects by three Chinese herbs. American Journal of Chinese Medicine. 1994, 22(2):97
26 J.L. Jin, J. Min-suk Development of a Radial Artery Pulse Wave Transducer for Diagnostic of Human Body Constitution. Proceedings of the 32nd International Symposium on Robotics, 2001:19~21
27 X.F. Teng, and Y.T. Zhang. Continuous and Noninvasive Estimation of Arterial Blood Pressure Using a Photoplethysmographic Approach, Proceedings of the
25th Annual International Conference of the IEEE EMBS, Cancun, Mexico, September 17-21, 2003: 3153~3156
28 D. Korpas, J. Hálek Device for Pulse Wave Measurement and Analysis. Physician and Technology. 2003, 34: 163~170
29 J.Y. Lee, J.H. Kim, and M.H. Lee. Design of Digital Hardware System for Pulse Signals. Journal of Medical Systems, 2001, 25(6): 385~394
30 Lau, O.Y. and A.T. Chwang. Relationship Between Wrist-Pulse Characteristics and Body Conditions. Fourteenth Engineering Mechanics Conference EM2000.American Society of Civil Engineers. May 21-24, 2000
31徐礼胜.中医脉象数字化研究.哈尔滨工业大学博士论文. 2006: 43~46
32张吉文,于永权. MC68HC05SR3研制的脉象血压仪系统.电子技术2000,1(1): 12~15
33刘兴旺,李训铭,岳沛平.小波变换在脉象信号消噪处理中的应用.计算机与现代化. 2007,1(5): 21~23
34张小虎,杨小建.基于模糊控制的固态下料系统的设计.自动化仪表. 2006,9(3): 11~14
35陈天如,邱恺.基于加权变换的传感器故障检测新方法.传感器技术. 2003,22(9): 59~62
36许小勇,钟太勇.三次样条插值函数的构造与Matlab实现.自动测量与控制. 2006,25(11): 76~78
37 F. Itakura, Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoustics, Speech, and Signal Proc. 1975, 23(1): 52~72
38 L. Rabiner, A. Rosenberg and S. Levinson. Considerations in Dynamic Time Warping Algorithms for Discrete Word Recognition. IEEE Trans. Acoustics, Speech, and Signal Proc., ASSP-26: 575~582.
39 J. Aach and G. Church. Aligning gene expression time series with time warping algorithms. Bioinformatics. 2001, 17(6):495~508
40 E.G. Caiani, A. Porta, G. Baselli, et al. Warped-average template technique to track on a cycle-by-cycle basis the cardiac filling phases on left ventricular volume. Proceedings of IEEE Computers in Cardiology, September, 1998:73~76
41 B. Huang, W. Kinsner. ECG frame classification using dynamic time warping.Proceedings of the 2002 Canadian Conference on Electrical & Computer Engineering, 2002:1105~1110
42 M. Schmill, T. Oates and P. Cohen. Learned models for continuous planning. Seventh International Workshop on Artificial Intelligence and Statistics, 1999: 278~282
43 D.M. Gavrila and L.S. Davis. Towards 3-D model-based tracking and recognition of human movement: a multi-view approach. International Workshop on Automatic Face and Gesture Recognition, Zurich, 1995: 21~47
44 Z.M. Kovacs-Vajna. A fingerprint verification system based on triangular matching and dynamic time warping. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000, 22(11):1266~1276
45 D. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. AAAI-94 Workshop on Knowledge Discovery in Databases, 1994:229~248.
46 F. Itakura, Minimum Prediction Residual Principle Applied to SpeechRecognition. IEEE Trans. Acoustics, Speech, and Signal Proc., 1975, ASSP-23(1): 52~72
47 H. Sakoe and Chiba, S. Dynamic Programming Algorithm Optimization For Spoken Word Recognition. IEEE Trans. Acoustics, Speech, and Signal Proc., 1978, ASSP-26: 43~49
48 E.J. Keogh and M.J. Pazzani. Derivative Dynamic Time Warping. First SIAM International Conference on Data Mining, Chicago, 2001: 31~34
2赵恩俭.中医脉诊学.天津科学技术出版社. 1990:1~18
3黄世林,孙明异.中医脉象研究.人民卫生出版社, 1986. 2:1~2
4李时珍[明].濒湖脉学白话解.人民卫生出版社, 1978:11~62
5王叔和[晋].脉经.人民卫生出版社, 1982:7~34
6 M. Kato, T. Koeda, et al., Measurement of digital arterial blood pressure and its recording on usual electrocardiograph paper. Tohoku J. Exp. Med., 1977,123:307~314
7 E.M.Simmons, H.Leader, S.A.Friedman, B.Davis, D.Lee, T.Winsor, C.A.Caceres. A computer program for the peripheral pulse wave. The American Journal of cardiology. 1967, 19:827~831
8 E.D. Freis and M.C. Kyle. Computer analysis of carotid and brachial pulse waves. The American Journal of Cardiology. November 1968, 22:691~695
9 S. Nisssila, M. Sorvisto. Non-invasive blood pressure measurement based on the electronic palpation method. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. HongKong, China, 1998, 20(4):1723~1726
10 B.P. Wlundell. Pulse wave patterns in patent ductus arteriosus.Archives of Disease in Childhood. 1983, 58:682~685
11傅骢远,牛欣.中医脉象今释.华夏出版社, 1993:3~51
12费兆馥.现代中医脉诊学.人民卫生出版社, 2003:15~48`
13李凌,王志中.脉搏波变异型及其心率变异性关系研究.上海交通大学博士论文. 2000: 12~14
14王炳和,相敬林.人体脉搏系统建模与脉搏信息处理方法研究.西北工业大学博士论文. 1999: 17~21
15 W.A. Lu, Y.Y. Lin Wang, W.K. Wang. Pulse analysis of patients with severs liver problems. IEEE Engineering in Medicine and Biology Magazine. Jan/Feb 1999, 18:73~75
16金伟.金氏脉学.山东科学技术出版社, 2000:3~14
17何素荣,刘世斌.临床脉图诊断学.人民军医出版社, 2003:14~61
18刘冠军.中华脉诊.中国中医药出版社, 2002:77~91
19杨天权.中医脉学应用新进展—附60则脉案分析.上海交通大学出版社, 1994:1~13
20徐迪华,徐剑秋.中医量化诊断.江苏科学技术出版社, 1997:31~75
21 S.H. Yoon, Y. Koga, I. Matsumoto, and E. Ikezono. An objective method of pulse diagnosis. American Journal of Chinese medicine. 1987, 15(3~4):147~153
22 B. Michael, and M. Michael. Instrument assisted pulse evaluation in the acupuncture practice. American Journal of Acupuncture. 1986, 14(3):255~259
23 Y.Z. Yoon, M.H. Lee and K.S. Soh. Pulse type classification by varying contact pressure. IEEE Engineering in Medicine and Biology. November/December 2000, 19:106~110
24 S. He. et al. Objectifying of pulse-taking. Journal of Japanese Eastern Medicine Society. 1977, 27(4):7
25 W.K. Wang, H.L. Chen, T.L. Hsu, et al. Alternations in human subjects by three Chinese herbs. American Journal of Chinese Medicine. 1994, 22(2):97
26 J.L. Jin, J. Min-suk Development of a Radial Artery Pulse Wave Transducer for Diagnostic of Human Body Constitution. Proceedings of the 32nd International Symposium on Robotics, 2001:19~21
27 X.F. Teng, and Y.T. Zhang. Continuous and Noninvasive Estimation of Arterial Blood Pressure Using a Photoplethysmographic Approach, Proceedings of the
25th Annual International Conference of the IEEE EMBS, Cancun, Mexico, September 17-21, 2003: 3153~3156
28 D. Korpas, J. Hálek Device for Pulse Wave Measurement and Analysis. Physician and Technology. 2003, 34: 163~170
29 J.Y. Lee, J.H. Kim, and M.H. Lee. Design of Digital Hardware System for Pulse Signals. Journal of Medical Systems, 2001, 25(6): 385~394
30 Lau, O.Y. and A.T. Chwang. Relationship Between Wrist-Pulse Characteristics and Body Conditions. Fourteenth Engineering Mechanics Conference EM2000.American Society of Civil Engineers. May 21-24, 2000
31徐礼胜.中医脉象数字化研究.哈尔滨工业大学博士论文. 2006: 43~46
32张吉文,于永权. MC68HC05SR3研制的脉象血压仪系统.电子技术2000,1(1): 12~15
33刘兴旺,李训铭,岳沛平.小波变换在脉象信号消噪处理中的应用.计算机与现代化. 2007,1(5): 21~23
34张小虎,杨小建.基于模糊控制的固态下料系统的设计.自动化仪表. 2006,9(3): 11~14
35陈天如,邱恺.基于加权变换的传感器故障检测新方法.传感器技术. 2003,22(9): 59~62
36许小勇,钟太勇.三次样条插值函数的构造与Matlab实现.自动测量与控制. 2006,25(11): 76~78
37 F. Itakura, Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoustics, Speech, and Signal Proc. 1975, 23(1): 52~72
38 L. Rabiner, A. Rosenberg and S. Levinson. Considerations in Dynamic Time Warping Algorithms for Discrete Word Recognition. IEEE Trans. Acoustics, Speech, and Signal Proc., ASSP-26: 575~582.
39 J. Aach and G. Church. Aligning gene expression time series with time warping algorithms. Bioinformatics. 2001, 17(6):495~508
40 E.G. Caiani, A. Porta, G. Baselli, et al. Warped-average template technique to track on a cycle-by-cycle basis the cardiac filling phases on left ventricular volume. Proceedings of IEEE Computers in Cardiology, September, 1998:73~76
41 B. Huang, W. Kinsner. ECG frame classification using dynamic time warping.Proceedings of the 2002 Canadian Conference on Electrical & Computer Engineering, 2002:1105~1110
42 M. Schmill, T. Oates and P. Cohen. Learned models for continuous planning. Seventh International Workshop on Artificial Intelligence and Statistics, 1999: 278~282
43 D.M. Gavrila and L.S. Davis. Towards 3-D model-based tracking and recognition of human movement: a multi-view approach. International Workshop on Automatic Face and Gesture Recognition, Zurich, 1995: 21~47
44 Z.M. Kovacs-Vajna. A fingerprint verification system based on triangular matching and dynamic time warping. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2000, 22(11):1266~1276
45 D. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. AAAI-94 Workshop on Knowledge Discovery in Databases, 1994:229~248.
46 F. Itakura, Minimum Prediction Residual Principle Applied to SpeechRecognition. IEEE Trans. Acoustics, Speech, and Signal Proc., 1975, ASSP-23(1): 52~72
47 H. Sakoe and Chiba, S. Dynamic Programming Algorithm Optimization For Spoken Word Recognition. IEEE Trans. Acoustics, Speech, and Signal Proc., 1978, ASSP-26: 43~49
48 E.J. Keogh and M.J. Pazzani. Derivative Dynamic Time Warping. First SIAM International Conference on Data Mining, Chicago, 2001: 31~34