心电信号在线数据知识化研究
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摘要
心脏病是威胁人类生命的主要疾病之一,长期以来,心脏病的研究一直是医学界的重要课题。心电图记录心脏生理电活动,其中蕴涵着丰富的反映心脏节律及其电传导的生理和病理信息,是诊断心脏疾病、评价心脏功能的重要依据之一。微处理器应用于心电图辅助分析,提高了分析的速度,促进了分析算法的发展,但由于心电信号分析的复杂性,在准确性等方面,仍有待于进一步的提高。
本文分析了心电图诊断,其实质就是一个从心电数据中获取心脏状态知识的过程,因此将数据知识化的概念引入心电信号处理中。在此基础上,设计了基于数据知识化的辅助分析算法,将小波变换作为心电特征的检测算法,聚类分析用于分析QRS波群,单独一搏分析和串诊断相结合,对MIT-BIH标准心电数据库中心律失常部分类别能有效识别。
全文共分为六章:
第一章:介绍了心电图的基本知识、传统诊断过程和临床用途,阐述了心电图数据分析的过程和研究现状,并简要介绍了数据知识化的发展和应用,提出了将其引入心电信号分析过程中,最后指出本论文需要研究的内容。
第二章:首先分析心电信号中存在的各类干扰和噪声,并讨论了产生原因与特点,然后对其中工频干扰、基线漂移和高频干扰三类噪声,设计了相应的检测和消除算法,以提高心电信号的信噪比。
第三章:介绍了作为特征检测算法的小波变换基本原理,并阐述了该分析方法所具有的三个特性,接着设计了QRS、P和T波特征的小波变换检测算法,最后用标准心电数据库对检测算法进行了检验。
第四章:对聚类分析的基本知识进行了介绍,然后针对该方法和心电信号的特点,确定了聚类分析的处理对象,接着重点设计了适合于对QRS波群分析的聚类算法,最后用标准心电数据库对聚类算法进行了评估,并分析了聚类结果。
第五章:阐述了心律失常的产生原因,并介绍了主要的心律失常类别,然后分别对诊断分析的单独一搏分析和串诊断进行了介绍,并对部分心律失常类别进行了初步诊断。
第六章:对论文工作进行了总结,并对进一步的工作提出了自己的想法。
The heart disease is one of the most prominent disease threatening the life of human being. Study of heart disease has for a long time been an important issue in medicine. Electrocardiograph(ECG) records the activity of physiological electricity about heart, which hides abundant information of physiology and pathology reflecting the rhythm and conduction, and becomes an important basis for diagnosing the diseases and evaluating the function of heart. Microprocessor has been applied to ECG auxiliary diagnosis, which has improved the speed of analysis and promoted the development of analyzing algorithm. For the complexity of ECG, the accuracy of auxiliary diagnosis should be improved.The thesis analyzes the process of ECG diagnosis, which is essentially an acquisition of status knowledge from ECG data, so the concept of data knowledge discovery is introduced to processing the signal of ECG. On that basis, a series of auxiliary analyzing algorithms based on data knowledge discovery is designed, including wavelet transforms as detecting algorithm, clustering analysis being used to analyze QRS complex, combining one beat and final string diagnosis. These algorithms can distinguish part kinds of arrhythmia of the MIT-BIH standard ECG data-base.This thesis consists of six sections as follows:Chapter one firstly introduces the basic knowledge, traditional diagnosis process and clinic application of ECG, then expounds the process and recent development of ECG analysis, and briefly introduces the development and application of data knowledge discovery, which is introduced to processing the signal of ECG. At last, the contents of the thesis are outlined.In second section, firstly all types of interference and noises existing in the signal of ECG are analyzed, and which of the reasons and the characteristics are discussed. Detecting and eliminating algorithms are designed respectively against three kinds of noise, power-line interference, base-line wander and high frequency interference, to improve the signal-to-noise ratio of ECG signal.Chapter three introduces the basic theory of wavelet transforms which is used as the detecting algorithm of the features, and states three characteristics of this method,
本文分析了心电图诊断,其实质就是一个从心电数据中获取心脏状态知识的过程,因此将数据知识化的概念引入心电信号处理中。在此基础上,设计了基于数据知识化的辅助分析算法,将小波变换作为心电特征的检测算法,聚类分析用于分析QRS波群,单独一搏分析和串诊断相结合,对MIT-BIH标准心电数据库中心律失常部分类别能有效识别。
全文共分为六章:
第一章:介绍了心电图的基本知识、传统诊断过程和临床用途,阐述了心电图数据分析的过程和研究现状,并简要介绍了数据知识化的发展和应用,提出了将其引入心电信号分析过程中,最后指出本论文需要研究的内容。
第二章:首先分析心电信号中存在的各类干扰和噪声,并讨论了产生原因与特点,然后对其中工频干扰、基线漂移和高频干扰三类噪声,设计了相应的检测和消除算法,以提高心电信号的信噪比。
第三章:介绍了作为特征检测算法的小波变换基本原理,并阐述了该分析方法所具有的三个特性,接着设计了QRS、P和T波特征的小波变换检测算法,最后用标准心电数据库对检测算法进行了检验。
第四章:对聚类分析的基本知识进行了介绍,然后针对该方法和心电信号的特点,确定了聚类分析的处理对象,接着重点设计了适合于对QRS波群分析的聚类算法,最后用标准心电数据库对聚类算法进行了评估,并分析了聚类结果。
第五章:阐述了心律失常的产生原因,并介绍了主要的心律失常类别,然后分别对诊断分析的单独一搏分析和串诊断进行了介绍,并对部分心律失常类别进行了初步诊断。
第六章:对论文工作进行了总结,并对进一步的工作提出了自己的想法。
The heart disease is one of the most prominent disease threatening the life of human being. Study of heart disease has for a long time been an important issue in medicine. Electrocardiograph(ECG) records the activity of physiological electricity about heart, which hides abundant information of physiology and pathology reflecting the rhythm and conduction, and becomes an important basis for diagnosing the diseases and evaluating the function of heart. Microprocessor has been applied to ECG auxiliary diagnosis, which has improved the speed of analysis and promoted the development of analyzing algorithm. For the complexity of ECG, the accuracy of auxiliary diagnosis should be improved.The thesis analyzes the process of ECG diagnosis, which is essentially an acquisition of status knowledge from ECG data, so the concept of data knowledge discovery is introduced to processing the signal of ECG. On that basis, a series of auxiliary analyzing algorithms based on data knowledge discovery is designed, including wavelet transforms as detecting algorithm, clustering analysis being used to analyze QRS complex, combining one beat and final string diagnosis. These algorithms can distinguish part kinds of arrhythmia of the MIT-BIH standard ECG data-base.This thesis consists of six sections as follows:Chapter one firstly introduces the basic knowledge, traditional diagnosis process and clinic application of ECG, then expounds the process and recent development of ECG analysis, and briefly introduces the development and application of data knowledge discovery, which is introduced to processing the signal of ECG. At last, the contents of the thesis are outlined.In second section, firstly all types of interference and noises existing in the signal of ECG are analyzed, and which of the reasons and the characteristics are discussed. Detecting and eliminating algorithms are designed respectively against three kinds of noise, power-line interference, base-line wander and high frequency interference, to improve the signal-to-noise ratio of ECG signal.Chapter three introduces the basic theory of wavelet transforms which is used as the detecting algorithm of the features, and states three characteristics of this method,
引文
[1] 山东医学院附属医院.实用心电图学.济南:山东科学技术出版社,1979.
[2] 许克诚.临床心电图学教程.北京:人民卫生出版社,1982.
[3] 周玉珍.简明心电图手册.北京:人民军医出版社,1998.
[4] 尉挺,徐国铭,赵从文.心电图诊断学.西安:陕西科学技术出版社,1983.
[5] 朱红,刘雅洁.全科医生辅助诊断手册.济南:山东科学技术出版社,2003.
[6] Chr. Zywietz, J.L. Willems, P. Arnaud. Stability of Computer ECG Amplitude Measurements in the Presence of Noise. Computers and Biomedical Research, 1990, 23:10-23.
[7] Ewa Pietka. Feature Extraction in Computerized Approach to the ECG Analysis. Pattern Recognition, 1991, 24(2):139-146.
[8] 田爱军,王爱莉.一种检测与分析QRS波的算法.中国医疗器械杂志,1992,16(3):140-147.
[9] Karsten Sternickel. Automatic pattern recognition in ECG time series. Computer Methods and Programs in Biomedicine. 2002, 109-115.
[10] 杨海威,詹永麒,胡伟国.12导联心电图P波检测算法.北京生物医学工程,2002,21(2):102-105.
[11] 邱庆军,蒋景英,虞启琏.QRS波检测方法的回顾与新进展.中国医疗器械杂志,1999,23(6):344-347.
[12] J. Pan, W. J. Tompkins. A real-time QRS detection algorithm. IEEE Trans. On BME, 1985, 32(3):230-236.
[13] S.M. Collins, R.C. Arzbaecher. An efficient algorithm for waveform. Proceeding of the IEEE Engineering in Medicine and Biology Society, 1993, 10: 330-331.
[14] 肖咏梅,陈杭,葛霁光.心电自动分析技术.生物医学杂志,2000,17(3):339-342
[15] 孔玉,张乐平,周洪堂.QRS波群检测方法的研究进展.医学信息,2004,17(1):62-63
[16] 范晓东,朱泽煌.心电特征点定位算法.北京生物医学工程,1995,15(1)
[17] 杨福生.小波变换的工程分析与应用.北京:科学出版社,2001.
[18] 李翠微,郑崇勋,袁超伟.ECG信号的小波变换检测方法.中国生物医学工程学报,1995,14(1):59-66.
[19] Cuiwei Li, Chongxun Zheng, Changfeng Tai. Detection of ECG Characteristic Points Using Wavelet Transforms. IEEE Transacion Biomedical Engineering, 1995, 42(1):21-28.
[20] Mallat S, Zhong S. Characterization of signals from multiscale edges. IEEE Trans Pattern Anal Mach Intell, 19992,14(7):710-732
[21] 肖咏梅.心电信号复杂分析方法学研究[硕士学位论文].杭州:浙江大学,1999.
[22] 黄光谷.概率统计方法导论.武汉:中国地址大学出版社,1994.
[23] Thakor NV, Natarajan A, Tomaselli G. Multiway sequential hypothesis testing for tachyarrhythmia dicrimination. IEEE Transaction Biomedical Engineering, 1994, 41(5): 480-487.
[24] 贺仲雄.模糊数学及其应用.天津:天津科学技术出版社,1982.
[25] 李相镐,李洪兴,陈世权.模糊聚类分析及其应用.贵阳:贵州科技出版社,1991.
[26] 胡守恒,余少波,戴葵.神经网络导论.长沙:国防科学技术大学出版社,1993.
[27] 宓霞.神经网络在心电图诊断技术上的应用.江苏理工大学学报.1996,17(2):106-110.
[28] 宁新宝,黄晓林,徐寅林,韩桂明.神经网络分析方法用于心脏病诊断的研究.生物医学工程学杂志,2004,21(2):284-287.
[29] Tsai Y S, Hung B N. Tung S F. An Experiment on ECG Classification Using Backpropagation Neural Networks. Proceedings of the 12th Annual International Conference of the IEEE Engineering in Medicaine and Biologye Society, 1990, 1463-1464.
[30] Linnerbank A C, Sippens Groenewegen A, Grimbergen C A. Artificial Neural Networks Applied in Multiple Lead Electrocardiography: Rapid Quantitative Classification of Ventricular Tachycardia QRS Integral Patterns. Proceeding of the 12th Annual International Conference of the IEEE Engineering in Medicaine and Biologye Society, 1990,1461-1462.
[31] G Bortolan, R Degani, JL Willems. ECG Classification with Neural Networks and Cluster Analysis. Computers in Cardiology Proceedings, 1991,177-180.
[32] 何新贵.知识处理与专家系统.北京:国防工业出版社,1990.
[33] 张全寿,周建峰.专家系统建造原理及方法.北京:中国铁道出版社,1992.
[34] 胡运发.数据与知识工程导论.北京:清华大学出版社,2003.
[35] 朱明.数据挖掘.合肥:中国科学技术大学出版,2002.
[36] 胡文丰,张正国.生物医学数据挖掘.国外医学生物医学工程分册,2003,26(1):11-15.
[37] 朱泽煌,胡广书,郭恒,崔子经.MIT-BIH心电数据库的开发及用作检测标准.中国生物医学工程学报,1993,12(4):244—250.
[38] 胡广书,朱泽煌.心电图的计算机分析.上海生物医学工程,1995,16(4):59-60
[39] M.L. Ahlstromand, W.J. Tompkins. Digital Filters for Real-Time ECG Signal Processing Using Microprocessors. IEEE TRANS on BME, 1985, 32 (9).
[40] Nitish V. Thakor, Yi-Sheng Zhu. Applications of Adaptive Filtering to ECG Analysis: Noise Cancellation and Arrhythmia Detection. IEEE Transactions on Biomedical Engineering, 1991, 38(8):785-793.
[41] 武永成,杨玉星.一种适于实时滤除ECG工频干扰新方法,中国医疗器械杂志,1999,23(3):145-148.
[42] J.A. Van Alstey. Removal of Base-line Wander and Power-line Interference from the ECG by an Efficient FIR Filter with a Reduced Number of Taps. IEEE TRANS on BME, 1985, 32: 1052-1060.
[43] 俞卞章.数字信号处理.西安:西北工业大学出版社,2002
[44] 周静.心电信号中工频干扰的消除.生物医学工程研究,2003,22(4):61-64.
[45] 王镇,蔡萍.用于去除心电信号中工频干扰数字滤波技术.电子测量技术,2000,2:43-47.
[46] 李刚,刘巍,虞启琏.抑制工频干扰及基线漂移的快速算法.中国生物医学工程学报,2000,19(1):99-103.
[47] 李刚,林凌,虞启琏.滤除工频干扰的自适应相干模板法.中国生物医学工程学报.1997,16(3):280-283.
[48] 吴翔明,吴筏辉,宋水孝.心电图基线漂移成因及其消除的实验研究.心电学杂志.1989,8(3):191-192.
[49] 田玉荣.比较两种去基线漂移的滤波算法.中国医学物理学杂志.1998,15(3):181-182.
[50] 孔令锋,谢锡城,卢志远.矫正心电信号中基线漂移的数字滤波技术及其实现.中国医疗器械信息,2003,6:12-14.
[51] 王健,靳奉祥,史玉峰.模式识别中特征选择与聚类分析.测绘工程,2002,11(2):21-24.
[52] 李金宗.模式识别导论.北京:高等教育出版社,1994.
[2] 许克诚.临床心电图学教程.北京:人民卫生出版社,1982.
[3] 周玉珍.简明心电图手册.北京:人民军医出版社,1998.
[4] 尉挺,徐国铭,赵从文.心电图诊断学.西安:陕西科学技术出版社,1983.
[5] 朱红,刘雅洁.全科医生辅助诊断手册.济南:山东科学技术出版社,2003.
[6] Chr. Zywietz, J.L. Willems, P. Arnaud. Stability of Computer ECG Amplitude Measurements in the Presence of Noise. Computers and Biomedical Research, 1990, 23:10-23.
[7] Ewa Pietka. Feature Extraction in Computerized Approach to the ECG Analysis. Pattern Recognition, 1991, 24(2):139-146.
[8] 田爱军,王爱莉.一种检测与分析QRS波的算法.中国医疗器械杂志,1992,16(3):140-147.
[9] Karsten Sternickel. Automatic pattern recognition in ECG time series. Computer Methods and Programs in Biomedicine. 2002, 109-115.
[10] 杨海威,詹永麒,胡伟国.12导联心电图P波检测算法.北京生物医学工程,2002,21(2):102-105.
[11] 邱庆军,蒋景英,虞启琏.QRS波检测方法的回顾与新进展.中国医疗器械杂志,1999,23(6):344-347.
[12] J. Pan, W. J. Tompkins. A real-time QRS detection algorithm. IEEE Trans. On BME, 1985, 32(3):230-236.
[13] S.M. Collins, R.C. Arzbaecher. An efficient algorithm for waveform. Proceeding of the IEEE Engineering in Medicine and Biology Society, 1993, 10: 330-331.
[14] 肖咏梅,陈杭,葛霁光.心电自动分析技术.生物医学杂志,2000,17(3):339-342
[15] 孔玉,张乐平,周洪堂.QRS波群检测方法的研究进展.医学信息,2004,17(1):62-63
[16] 范晓东,朱泽煌.心电特征点定位算法.北京生物医学工程,1995,15(1)
[17] 杨福生.小波变换的工程分析与应用.北京:科学出版社,2001.
[18] 李翠微,郑崇勋,袁超伟.ECG信号的小波变换检测方法.中国生物医学工程学报,1995,14(1):59-66.
[19] Cuiwei Li, Chongxun Zheng, Changfeng Tai. Detection of ECG Characteristic Points Using Wavelet Transforms. IEEE Transacion Biomedical Engineering, 1995, 42(1):21-28.
[20] Mallat S, Zhong S. Characterization of signals from multiscale edges. IEEE Trans Pattern Anal Mach Intell, 19992,14(7):710-732
[21] 肖咏梅.心电信号复杂分析方法学研究[硕士学位论文].杭州:浙江大学,1999.
[22] 黄光谷.概率统计方法导论.武汉:中国地址大学出版社,1994.
[23] Thakor NV, Natarajan A, Tomaselli G. Multiway sequential hypothesis testing for tachyarrhythmia dicrimination. IEEE Transaction Biomedical Engineering, 1994, 41(5): 480-487.
[24] 贺仲雄.模糊数学及其应用.天津:天津科学技术出版社,1982.
[25] 李相镐,李洪兴,陈世权.模糊聚类分析及其应用.贵阳:贵州科技出版社,1991.
[26] 胡守恒,余少波,戴葵.神经网络导论.长沙:国防科学技术大学出版社,1993.
[27] 宓霞.神经网络在心电图诊断技术上的应用.江苏理工大学学报.1996,17(2):106-110.
[28] 宁新宝,黄晓林,徐寅林,韩桂明.神经网络分析方法用于心脏病诊断的研究.生物医学工程学杂志,2004,21(2):284-287.
[29] Tsai Y S, Hung B N. Tung S F. An Experiment on ECG Classification Using Backpropagation Neural Networks. Proceedings of the 12th Annual International Conference of the IEEE Engineering in Medicaine and Biologye Society, 1990, 1463-1464.
[30] Linnerbank A C, Sippens Groenewegen A, Grimbergen C A. Artificial Neural Networks Applied in Multiple Lead Electrocardiography: Rapid Quantitative Classification of Ventricular Tachycardia QRS Integral Patterns. Proceeding of the 12th Annual International Conference of the IEEE Engineering in Medicaine and Biologye Society, 1990,1461-1462.
[31] G Bortolan, R Degani, JL Willems. ECG Classification with Neural Networks and Cluster Analysis. Computers in Cardiology Proceedings, 1991,177-180.
[32] 何新贵.知识处理与专家系统.北京:国防工业出版社,1990.
[33] 张全寿,周建峰.专家系统建造原理及方法.北京:中国铁道出版社,1992.
[34] 胡运发.数据与知识工程导论.北京:清华大学出版社,2003.
[35] 朱明.数据挖掘.合肥:中国科学技术大学出版,2002.
[36] 胡文丰,张正国.生物医学数据挖掘.国外医学生物医学工程分册,2003,26(1):11-15.
[37] 朱泽煌,胡广书,郭恒,崔子经.MIT-BIH心电数据库的开发及用作检测标准.中国生物医学工程学报,1993,12(4):244—250.
[38] 胡广书,朱泽煌.心电图的计算机分析.上海生物医学工程,1995,16(4):59-60
[39] M.L. Ahlstromand, W.J. Tompkins. Digital Filters for Real-Time ECG Signal Processing Using Microprocessors. IEEE TRANS on BME, 1985, 32 (9).
[40] Nitish V. Thakor, Yi-Sheng Zhu. Applications of Adaptive Filtering to ECG Analysis: Noise Cancellation and Arrhythmia Detection. IEEE Transactions on Biomedical Engineering, 1991, 38(8):785-793.
[41] 武永成,杨玉星.一种适于实时滤除ECG工频干扰新方法,中国医疗器械杂志,1999,23(3):145-148.
[42] J.A. Van Alstey. Removal of Base-line Wander and Power-line Interference from the ECG by an Efficient FIR Filter with a Reduced Number of Taps. IEEE TRANS on BME, 1985, 32: 1052-1060.
[43] 俞卞章.数字信号处理.西安:西北工业大学出版社,2002
[44] 周静.心电信号中工频干扰的消除.生物医学工程研究,2003,22(4):61-64.
[45] 王镇,蔡萍.用于去除心电信号中工频干扰数字滤波技术.电子测量技术,2000,2:43-47.
[46] 李刚,刘巍,虞启琏.抑制工频干扰及基线漂移的快速算法.中国生物医学工程学报,2000,19(1):99-103.
[47] 李刚,林凌,虞启琏.滤除工频干扰的自适应相干模板法.中国生物医学工程学报.1997,16(3):280-283.
[48] 吴翔明,吴筏辉,宋水孝.心电图基线漂移成因及其消除的实验研究.心电学杂志.1989,8(3):191-192.
[49] 田玉荣.比较两种去基线漂移的滤波算法.中国医学物理学杂志.1998,15(3):181-182.
[50] 孔令锋,谢锡城,卢志远.矫正心电信号中基线漂移的数字滤波技术及其实现.中国医疗器械信息,2003,6:12-14.
[51] 王健,靳奉祥,史玉峰.模式识别中特征选择与聚类分析.测绘工程,2002,11(2):21-24.
[52] 李金宗.模式识别导论.北京:高等教育出版社,1994.