基于小波变换与模糊神经网络的ECG自动分析
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摘要
心血管疾病是当今危害人类健康的主要疾病之一,心电图(ECG)检查是临床诊断心血管疾病的重要方法。利用计算机对ECG信号进行自动准确的分析是国内外学者热衷的课题。ECG自动分析的任务主要包括对ECG信号进行滤波预处理、进行波形特征点的检测等,最终目的是对心电波形进行分类识别,根据其波形特点或者特征参数自动地做出疾病诊断。在各种心血管疾病中,室性早搏(PVC)是一种最常见的心律失常,它的实时、正确检测是ECG自动分析的一项重要技术,对提高心律失常检测、监护系统和动态心电图分析系统的性能起关键作用,对改善心脏病诊断有重要的实用价值。
利用小波变换进行ECG信号滤波处理与波形检测、用模糊推理技术及神经网络进行ECG分类识别等,在国内外已有不少报道,但算法还都不够完善。本课题就是要对小波变换和模糊神经网络在ECG自动分析中的应用,进行深入的研究和探讨,在总结前人工作的基础上,主要做了以下几方面的工作:
1.对基于小波变换的ECG信号滤波方法进行改进,即:将小波变换与自适应滤波技术相结合。这也是本论文的创新之处。经实验仿真验证,采用本文改进的滤波方法,可以在有效地去除噪声、干扰的同时,减少有用心电信息的损失,较好地保持心电信号的波形特征。
2.利用小波变换的信号奇异性检测理论,深入研究和探讨了ECG波形检测方法,即构造Marr小波滤波器,从等效滤波器的角度分析ECG信号的离散二进小波变换,通过对ECG信号小波变换模极大值列的探测,准确检测出R波、QRS波起止点、P波、T波等波形特征点。在R波检测中,采用了可变阈值、不应期、Lipschitz指数判据等策略,极大地提高了波形检测正确率。
3.进行室性早搏自动检测算法的研究:将模糊逻辑与神经网络进行有机结合,即利用具有模糊化输入、输出的前向神经网络的分类识别能力,实现ECG信号中正常心拍和室性早搏的自动识别,用MIT—BIH心电数据库中的样本数据对该模糊神经网络进行训练、检验,证明该网络具有较高的PVC自动识别率。
Cardiovascular diseases are one of the main diseases that endanger human's health nowadays. The electrocardiogram (ECG) examination is very important for clinical diagnosis of cardiovascular diseases. The application of computers at the accurate and automatic analysis of ECG has been a hotspot to researchers at home and overseas. The missions of the automatic analysis include the filter pretreatment of ECG signal and the examination of ECG characteristics etc. The final purpose is to classify ECG and diagnose diseases according to the characteristics of ECG. In various cardiovascular diseases, the Premature Ventricular Contract (PVC) is one of the most familiar diseases of the arrhythmia. Its real-time and accurate examination is an important technique of ECG automatic analysis. It operates a key function in increasing the performance of arrhythmia custody system and Holter ECG analysis system. It also has important practical value in improving the heart disease diagnosis.There have been had a lot of reports on the use of wavelet transform in carrying on the filter pretreatment of ECG signal and examining the ECG characteristics, and the use of fuzzy reasoning and artificial neutral network in classifying and identifying ECG. But these techniques aren't perfect. In this paper, the applications of wavelet transform and fuzzy neural network at ECG automatic analysis were further researched and discussed. We mainly did following several works on the base of summarizing the past research work.Firstly, a modified method of ECG filter based on wavelet transform was put forward. That is to combine the wavelet transform
and adaptive filter. It is an innovation of this paper. The experimental results demonstrated that the noises of ECG signal were successfully removed, the useful ECG information was perfectly preserved, and the modified denoising method was efficient and had better capability of filtering.Secondly, the ECG examination was further researched and discussed using the signal's singularity examination theory of wavelet transform. Namely, Marr wavelet filters were designed, analyzed the discrete binary wavelet transform of ECG signal from the point of equivalent filter, the R wave、 jumping-off point and end point of QRS wave、 P wave and T wave were accurately examined by using Marr wavelet transform at ECG signal and examining the maximum line of wavelet transform. In the examination of R wave, several strategies such as volatile thresholds, disapprobatory interval, and Lipschitz exponent so on, were used for increasing the correctness of ECG characteristic examination.Thirdly, the automatic examination of PVC was researched. The automatic identification of normal ECG and PVC were realized using the classification and identification abilities of artificial neutral network with fuzzy input and output. The fuzzy neural network was proved having better automatic identification ability, after training and testing the neural network using the ECG datum of MIT-BIH database.
利用小波变换进行ECG信号滤波处理与波形检测、用模糊推理技术及神经网络进行ECG分类识别等,在国内外已有不少报道,但算法还都不够完善。本课题就是要对小波变换和模糊神经网络在ECG自动分析中的应用,进行深入的研究和探讨,在总结前人工作的基础上,主要做了以下几方面的工作:
1.对基于小波变换的ECG信号滤波方法进行改进,即:将小波变换与自适应滤波技术相结合。这也是本论文的创新之处。经实验仿真验证,采用本文改进的滤波方法,可以在有效地去除噪声、干扰的同时,减少有用心电信息的损失,较好地保持心电信号的波形特征。
2.利用小波变换的信号奇异性检测理论,深入研究和探讨了ECG波形检测方法,即构造Marr小波滤波器,从等效滤波器的角度分析ECG信号的离散二进小波变换,通过对ECG信号小波变换模极大值列的探测,准确检测出R波、QRS波起止点、P波、T波等波形特征点。在R波检测中,采用了可变阈值、不应期、Lipschitz指数判据等策略,极大地提高了波形检测正确率。
3.进行室性早搏自动检测算法的研究:将模糊逻辑与神经网络进行有机结合,即利用具有模糊化输入、输出的前向神经网络的分类识别能力,实现ECG信号中正常心拍和室性早搏的自动识别,用MIT—BIH心电数据库中的样本数据对该模糊神经网络进行训练、检验,证明该网络具有较高的PVC自动识别率。
Cardiovascular diseases are one of the main diseases that endanger human's health nowadays. The electrocardiogram (ECG) examination is very important for clinical diagnosis of cardiovascular diseases. The application of computers at the accurate and automatic analysis of ECG has been a hotspot to researchers at home and overseas. The missions of the automatic analysis include the filter pretreatment of ECG signal and the examination of ECG characteristics etc. The final purpose is to classify ECG and diagnose diseases according to the characteristics of ECG. In various cardiovascular diseases, the Premature Ventricular Contract (PVC) is one of the most familiar diseases of the arrhythmia. Its real-time and accurate examination is an important technique of ECG automatic analysis. It operates a key function in increasing the performance of arrhythmia custody system and Holter ECG analysis system. It also has important practical value in improving the heart disease diagnosis.There have been had a lot of reports on the use of wavelet transform in carrying on the filter pretreatment of ECG signal and examining the ECG characteristics, and the use of fuzzy reasoning and artificial neutral network in classifying and identifying ECG. But these techniques aren't perfect. In this paper, the applications of wavelet transform and fuzzy neural network at ECG automatic analysis were further researched and discussed. We mainly did following several works on the base of summarizing the past research work.Firstly, a modified method of ECG filter based on wavelet transform was put forward. That is to combine the wavelet transform
and adaptive filter. It is an innovation of this paper. The experimental results demonstrated that the noises of ECG signal were successfully removed, the useful ECG information was perfectly preserved, and the modified denoising method was efficient and had better capability of filtering.Secondly, the ECG examination was further researched and discussed using the signal's singularity examination theory of wavelet transform. Namely, Marr wavelet filters were designed, analyzed the discrete binary wavelet transform of ECG signal from the point of equivalent filter, the R wave、 jumping-off point and end point of QRS wave、 P wave and T wave were accurately examined by using Marr wavelet transform at ECG signal and examining the maximum line of wavelet transform. In the examination of R wave, several strategies such as volatile thresholds, disapprobatory interval, and Lipschitz exponent so on, were used for increasing the correctness of ECG characteristic examination.Thirdly, the automatic examination of PVC was researched. The automatic identification of normal ECG and PVC were realized using the classification and identification abilities of artificial neutral network with fuzzy input and output. The fuzzy neural network was proved having better automatic identification ability, after training and testing the neural network using the ECG datum of MIT-BIH database.
引文
[1] 陈清启,杨庭树,卢喜烈等.心电图学.山东科学技术出版社,2002
[2] 许克成.临床心电图学教程.人民卫生出版社,1982
[3] 夏宏器,邓开伯.心律失常的临床分析与决策.中国协和医科大学出版社,2002
[4] 山东医学院附属医院.实用心电图学.山东科技出版社,1979
[5] 周群一.ECG心拍建模与分析识别.浙江大学博士学位论文,2004
[6] 杨钧国,李治安.现代心电图学.科学出版社.1997
[7] 李翠微,郑崇勋.ECG自动分析技术的发展.国外生物医学工程分册,1994,17(1):15—22
[8] 赵捷,华玫.一种滤除ECG基线漂移和交流干扰的简单算法.中国医疗器械,1991,15:262-63
[9] 孙京霞,白延强,杨玉星.一种抑制心电信号50Hz工频干扰的改进Levkov方法.航天医学与医学工程,2000,13(3):196-199
[10] 李刚,刘巍,虞启琏等.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
[11] 杨丰,余英林.小波变换在心电信号滤波处理中的应用研究.生物医学工程学杂志,1997,14(4):317-320
[12] 高清维,李海鹰,庄镇泉等.基于平稳小波变换的心电信号噪声消除方法.电子学报,2003,2(2):238-240
[13] 王笑梅,王辉.基于小波的ECG信号噪声消除.上海师范大学学报(自然科学版),2002,31(2):50—54
[14] 郑凯梅,余生晨.心电信号的小波变换滤波算法的改进.山东生物医学工程,2003,22(1):22-23
[15] 张艳丽,亓慧,陈振生.小波变换在ECG信号滤波中的应用研究.中国医学物理学杂志,2005,22(1):422—425
[16] 高艳,胡阳.基于数学形态学方法的心电图波形分离技术.生物医学工程学杂志,2001,18(1):55—59
[17] 冯劲松.使用数学形态学的QRS复合波检测方法.国外医学生物工程分册,1994:17(6):345-350
[18] Cuiwei Li,Chongxun Zheng. Detection of ECG characteristic points using wavelet transforms. IEEE Trans on BME, 1995,42(1): 21-28
[19] Sahambi J S, Tandon S N, Bhatt R K P.A new approach for in-line ECG characterization. Proceedings of the Fifteenth Southern Biomedical Engineering Conference, 1996: 409-411
[20] Sahambi J S, Tandon S N, Bhatt R K P. Using wavelet transforms for ECG characterization. IEEE Eng Med Biol, 1997, 16(1): 77-83
[21] 王超文,吕扬生,黄玉玺.用于ECG信号检测与重建的双正交样条小波滤波器.北京生物医学工程,2001,(1):25-28
[22] 余辉,张力新,吕扬生.基于小波变换的QRS波检测.生物医学工程与临床,2001,5(2):65—70
[23] 谢远国,余辉,吕扬生.基于多分辨率分析的心电图QRS波检测.医疗卫生装备.2003,9:5-6
[24] 杨毓英,史习智,陈光治等.小波变换用于ECG信号的R波实时探测.上海交通大学学报,1997,31(9):18-21
[25] 夏恒超,詹永麒,陈旭华.基于小波变换和波形信息的P波检测方法.北京生物医学工程,2003,22(1):27-29
[26] 史慧敏.改进的模糊BP网络在ECG自动识别与规则抽取中的应用研究.国防科学技术大学硕士学位论文,2002
[27] Zong W, Jiang D. Automated ECG rhythm analysis using fuzzy reasoning. Computer in Cardiology, 1998: 69-72
[28] 宗伟,蒋大宗.心律失常自动检测分类的模糊推理方法.95生物医学电子学联合年会论文集,1995:324-325
[29] Rosaria Silipo, Carlo Marchesi. Artificial neural networks for automatic ECG analysis. IEEE Trans. on Signal Processing, 1998, 46(8): 1417-1425
[30] 王涛,周荷琴,江朝辉等.混合型径向基函数神经网络检测 PVC.数据采集与处理,2000,15(1):1-5
[31] Braccini G. Self-organizing maps and Hermite functions for classification of ECG complexes. Computers in Cardiology, 1997: 425-428
[32] Risk M.R. Beat detection and classification of ECG using self organizing maps. Proc. Engineering in Medicine and Biology Society, 1997, 1: 89-91
[33] Ham FM, Soowhan Ban. Classification of cardiac arrhythmias using fuzzy ARTMAP. IEEE rrans, on BME, 1996, 43(4): 425-430
[34] 丁哨卫,王煦法,冯焕清等.基于自组织联想小波神经网络的ORS波聚类方法.中国科技技术大学学报,1998,28(3):310-315
[35] T. Olmez, Z. Dokur, E. Yazgan. Classification of ECG waveforms using a novel neural network. Proc. Engineering in Medicine and Biology Society, 1998, 3: 1616-1619
[36] 漆进,莫智文.基于粗糙集与神经网络结合的室性QRS波分类方法的研究.模式识别与人工智能,2002,15(1):121-124
[37] 刘建成,蔡湛宇.模糊神经网络方法在心脏病分型诊断中的应用.第一军医大学学报.2001,21(3):219-223
[38] Stanislaw Osowski, Tran Hoai Linh. ECG Beat Recognition Using Fuzzy Hybrid Neural Network. IEEE Transactions on Biomedical gngineering, 2001, 48(11): 1265-1271
[39] 彭玉华.小波变换与工程应用.科学出版社,1999
[40] 张永红.ECG信号中QRS波自动分析技术的研究.上海交通大学硕士学位论文,2003
[41] N. Nikolave, Z. Nikolov. Wavelet domain wiener filter for ECG denoising using improved signal estimate. Speech and Signal Processing, 2000, 6: 3578—3581
[42] 高育林,罗永光,李伟鹏.诱发脑电的滤波—基于小波分析和维纳滤波.中国医学物理学杂志,1996,13(4):250—254
[43] Park K L, Lee K J, Yooh H R. Application of a wavelet adaptive filter to minimize distortion of the ST-segment. Med. Biol. Eng. Comput, 1998, 36: 581—586
[44] Nurgun Erdol. Wavelet transform based adaptive filters:analysis and new results. IEEE Transactions on signal processing, 1996, 44(9): 2163—2171
[45] 吴兆熊.数字信号处理(下册).国防工业出版社,1985
[46] Mallat Stephen. Singularity detection and processing with wavelets. IEEE Trans Information Theory, 1992, 38(2): 617-643
[47] 阎平凡,张长水.人工神经网络与模拟进化计算.清华大学出版社,2000
[48] 王士同.神经模糊系统及其应用.北京航空航天大学出版社,1998
[49] 王西颖.模糊神经网络在遥感图象分类中的应用研究.北京科技大学硕士学位论文,2004
[50] 王洵.小波变换在心电信号分析中的应用.上海理工大学硕士学位论文,2001
[51] 沈利.心电信号处理与检测的研究.上海交通大学硕士学位论文,2003
[52] 洪玮.ECG波形分类算法研究.浙江大学硕士学位论文,2001
[53] 黄菁,周荷琴,杜克强等.模糊逻辑推理方法在室性早搏检测中的应用研究.中国科学技术大学学报,1998,28(3):356-361
[54] 周荷琴,冯焕清,孙景群等.室性早搏的实时检测方法研究.电路与系统学报,1999,4(4):86-90
[55] 王涛.混和型径向基函数神经网络检测PVC.数据采集与处理,2000,15(1):1-5
[56] Ham F.M. Classification of cardiac arrhythmias using fuzzy ARTMAP. IEEE Trans. On BME, 1996, 43(4): 425-429
[57] 王立斌,杨毓英,史习智.规则抽取的Fuzzy ARTMAP方法.数据采集与处理,1998,13(4):315-318
[58] 王弢,侯建军,吴北玲.FNN在心率失常判别中的研究.北方交通大学学报,2002,26(6):90-93
[59] 沈谦,王涛,张德龙.基于模糊前向神经网络的PVC识别方法.计算机工程与科学,2000,22(6):60-62
[60] 孙即祥.现代模式识别.国防科技大学出版社,2002
[2] 许克成.临床心电图学教程.人民卫生出版社,1982
[3] 夏宏器,邓开伯.心律失常的临床分析与决策.中国协和医科大学出版社,2002
[4] 山东医学院附属医院.实用心电图学.山东科技出版社,1979
[5] 周群一.ECG心拍建模与分析识别.浙江大学博士学位论文,2004
[6] 杨钧国,李治安.现代心电图学.科学出版社.1997
[7] 李翠微,郑崇勋.ECG自动分析技术的发展.国外生物医学工程分册,1994,17(1):15—22
[8] 赵捷,华玫.一种滤除ECG基线漂移和交流干扰的简单算法.中国医疗器械,1991,15:262-63
[9] 孙京霞,白延强,杨玉星.一种抑制心电信号50Hz工频干扰的改进Levkov方法.航天医学与医学工程,2000,13(3):196-199
[10] 李刚,刘巍,虞启琏等.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
[11] 杨丰,余英林.小波变换在心电信号滤波处理中的应用研究.生物医学工程学杂志,1997,14(4):317-320
[12] 高清维,李海鹰,庄镇泉等.基于平稳小波变换的心电信号噪声消除方法.电子学报,2003,2(2):238-240
[13] 王笑梅,王辉.基于小波的ECG信号噪声消除.上海师范大学学报(自然科学版),2002,31(2):50—54
[14] 郑凯梅,余生晨.心电信号的小波变换滤波算法的改进.山东生物医学工程,2003,22(1):22-23
[15] 张艳丽,亓慧,陈振生.小波变换在ECG信号滤波中的应用研究.中国医学物理学杂志,2005,22(1):422—425
[16] 高艳,胡阳.基于数学形态学方法的心电图波形分离技术.生物医学工程学杂志,2001,18(1):55—59
[17] 冯劲松.使用数学形态学的QRS复合波检测方法.国外医学生物工程分册,1994:17(6):345-350
[18] Cuiwei Li,Chongxun Zheng. Detection of ECG characteristic points using wavelet transforms. IEEE Trans on BME, 1995,42(1): 21-28
[19] Sahambi J S, Tandon S N, Bhatt R K P.A new approach for in-line ECG characterization. Proceedings of the Fifteenth Southern Biomedical Engineering Conference, 1996: 409-411
[20] Sahambi J S, Tandon S N, Bhatt R K P. Using wavelet transforms for ECG characterization. IEEE Eng Med Biol, 1997, 16(1): 77-83
[21] 王超文,吕扬生,黄玉玺.用于ECG信号检测与重建的双正交样条小波滤波器.北京生物医学工程,2001,(1):25-28
[22] 余辉,张力新,吕扬生.基于小波变换的QRS波检测.生物医学工程与临床,2001,5(2):65—70
[23] 谢远国,余辉,吕扬生.基于多分辨率分析的心电图QRS波检测.医疗卫生装备.2003,9:5-6
[24] 杨毓英,史习智,陈光治等.小波变换用于ECG信号的R波实时探测.上海交通大学学报,1997,31(9):18-21
[25] 夏恒超,詹永麒,陈旭华.基于小波变换和波形信息的P波检测方法.北京生物医学工程,2003,22(1):27-29
[26] 史慧敏.改进的模糊BP网络在ECG自动识别与规则抽取中的应用研究.国防科学技术大学硕士学位论文,2002
[27] Zong W, Jiang D. Automated ECG rhythm analysis using fuzzy reasoning. Computer in Cardiology, 1998: 69-72
[28] 宗伟,蒋大宗.心律失常自动检测分类的模糊推理方法.95生物医学电子学联合年会论文集,1995:324-325
[29] Rosaria Silipo, Carlo Marchesi. Artificial neural networks for automatic ECG analysis. IEEE Trans. on Signal Processing, 1998, 46(8): 1417-1425
[30] 王涛,周荷琴,江朝辉等.混合型径向基函数神经网络检测 PVC.数据采集与处理,2000,15(1):1-5
[31] Braccini G. Self-organizing maps and Hermite functions for classification of ECG complexes. Computers in Cardiology, 1997: 425-428
[32] Risk M.R. Beat detection and classification of ECG using self organizing maps. Proc. Engineering in Medicine and Biology Society, 1997, 1: 89-91
[33] Ham FM, Soowhan Ban. Classification of cardiac arrhythmias using fuzzy ARTMAP. IEEE rrans, on BME, 1996, 43(4): 425-430
[34] 丁哨卫,王煦法,冯焕清等.基于自组织联想小波神经网络的ORS波聚类方法.中国科技技术大学学报,1998,28(3):310-315
[35] T. Olmez, Z. Dokur, E. Yazgan. Classification of ECG waveforms using a novel neural network. Proc. Engineering in Medicine and Biology Society, 1998, 3: 1616-1619
[36] 漆进,莫智文.基于粗糙集与神经网络结合的室性QRS波分类方法的研究.模式识别与人工智能,2002,15(1):121-124
[37] 刘建成,蔡湛宇.模糊神经网络方法在心脏病分型诊断中的应用.第一军医大学学报.2001,21(3):219-223
[38] Stanislaw Osowski, Tran Hoai Linh. ECG Beat Recognition Using Fuzzy Hybrid Neural Network. IEEE Transactions on Biomedical gngineering, 2001, 48(11): 1265-1271
[39] 彭玉华.小波变换与工程应用.科学出版社,1999
[40] 张永红.ECG信号中QRS波自动分析技术的研究.上海交通大学硕士学位论文,2003
[41] N. Nikolave, Z. Nikolov. Wavelet domain wiener filter for ECG denoising using improved signal estimate. Speech and Signal Processing, 2000, 6: 3578—3581
[42] 高育林,罗永光,李伟鹏.诱发脑电的滤波—基于小波分析和维纳滤波.中国医学物理学杂志,1996,13(4):250—254
[43] Park K L, Lee K J, Yooh H R. Application of a wavelet adaptive filter to minimize distortion of the ST-segment. Med. Biol. Eng. Comput, 1998, 36: 581—586
[44] Nurgun Erdol. Wavelet transform based adaptive filters:analysis and new results. IEEE Transactions on signal processing, 1996, 44(9): 2163—2171
[45] 吴兆熊.数字信号处理(下册).国防工业出版社,1985
[46] Mallat Stephen. Singularity detection and processing with wavelets. IEEE Trans Information Theory, 1992, 38(2): 617-643
[47] 阎平凡,张长水.人工神经网络与模拟进化计算.清华大学出版社,2000
[48] 王士同.神经模糊系统及其应用.北京航空航天大学出版社,1998
[49] 王西颖.模糊神经网络在遥感图象分类中的应用研究.北京科技大学硕士学位论文,2004
[50] 王洵.小波变换在心电信号分析中的应用.上海理工大学硕士学位论文,2001
[51] 沈利.心电信号处理与检测的研究.上海交通大学硕士学位论文,2003
[52] 洪玮.ECG波形分类算法研究.浙江大学硕士学位论文,2001
[53] 黄菁,周荷琴,杜克强等.模糊逻辑推理方法在室性早搏检测中的应用研究.中国科学技术大学学报,1998,28(3):356-361
[54] 周荷琴,冯焕清,孙景群等.室性早搏的实时检测方法研究.电路与系统学报,1999,4(4):86-90
[55] 王涛.混和型径向基函数神经网络检测PVC.数据采集与处理,2000,15(1):1-5
[56] Ham F.M. Classification of cardiac arrhythmias using fuzzy ARTMAP. IEEE Trans. On BME, 1996, 43(4): 425-429
[57] 王立斌,杨毓英,史习智.规则抽取的Fuzzy ARTMAP方法.数据采集与处理,1998,13(4):315-318
[58] 王弢,侯建军,吴北玲.FNN在心率失常判别中的研究.北方交通大学学报,2002,26(6):90-93
[59] 沈谦,王涛,张德龙.基于模糊前向神经网络的PVC识别方法.计算机工程与科学,2000,22(6):60-62
[60] 孙即祥.现代模式识别.国防科技大学出版社,2002