远程监护系统心电信号特征快速提取方法研究
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摘要
由于生活水平的提高,近年来对居民的医疗保障关注程度也在加强,特别是对于医疗设施水平的要求也越来越高。作为诊断心血管疾病的主要依据心电信号的实时监测,也是近年来国内外关注的焦点。心血管疾病患者一旦犯病很快就会危及生命,所以必须要有一种系统能够及时的将患者的病情反馈到医院,让患者能得到及时的救治。因此,研究远程心电监护系统是十分有意义的。
本文针对患者随身携带的心电监护器采用了无线通信来实现心电信号的远距离传输,由于在线监测的数据量大,须首先提取异常数据,以便上传分析。该系统主要是围绕心电信号的去噪和特征提取等问题进行深入分析,同时根据心电信号的特点选择了小波分析理论对心电信号进行处理,完成异常信号的提取。
在用小波对信号进行去噪的过程中,本文选用了二次样条小波作为小波基函数,对三种主要噪声本文采用一种改进的软、硬阈值折中的方法去处肌电干扰和工频干扰,经过仿真实验,效果良好。
在心电信号的特征提取中,本文分别检测出P波、T波和QRS波的起点终点,并分别做了仿真,效果理想。特别是对R波峰值的的检测,本文采用了R波快速检测方法,R波峰值的检测效果明显提高,对于S-T波特征参数的提取利用J+X法检测特征点,为后期疾病的诊断提供重要的依据。
最后本文对远程心电系统进行了设计。选择了无线通信来实现远程心电监控系统,然后以Visual Studio 2005为开发平台,以SQL server 2000为后台数据库,实现了该监控软件。
As living standards improving, the degree of concern for people's health care has also been strengthened in recent years, particularly the demands for medical facilities level are also increasing. The extraction of ECG,which is the main basis for diagnosis of cardiovascular disease, is also the focus of attention at home and abroad in recent years. As the ECG signal is weak and are vulnerable to noise, the extraction becomes more difficult, what is more, long-distance transmission has higher demands for getting rid of the noise.'However, once someone falls ill with heart disease,his life will soon be threatened. As a result, there is necessary to be a system to timely feedback the condition of patients to the hospital, then patients can receive timely treatment. Therefore, it is very meaningful to study remote ECG monitoring system.
This paper mainly uses a wireless communication to achieve long distance transmission of ECG preparing for the diagnosis behind. The system is mainly doing in-depth analysis around the problems such as the ECG Denoising, feature extraction and so on. In addition, according to ECG characteristics the wavelet analysis theory is chosen to process the ECG signal, which is the main task of the system.
In the process of signal denoising by using wavelet, the quadratic spline wavelet is chosen as wavelet basis function. In order to eliminate the three major noise in this paper, such as EMG interference and Power-line interference and so on, an improved compromise of hard and soft threshold method is applied. And the result of the simulation is good.
In the progress of ECG feature extraction, this paper make presentation separately of the detetion of P wave, T wave and the start/end of QRS wave and make simulation separately, the results of which are satisfactory. In particular, to dtecte of wave peak, this paper applies the combination of the wavelet analysis and variable threshold method. The detection effect of the wave peak is improved obviously. As to extract the characteristic parameters of S-T wave, the J+X method is adopted to detect the feature points, to provide important evidence for the late diagnosis of the disease.
Finally, the remote ECG system is designed. The wireless communication is chosen to achieve the long-range ECG monitoring system, which is achieved through Visual Studio 2005, SQL server 2000 as the background database.
本文针对患者随身携带的心电监护器采用了无线通信来实现心电信号的远距离传输,由于在线监测的数据量大,须首先提取异常数据,以便上传分析。该系统主要是围绕心电信号的去噪和特征提取等问题进行深入分析,同时根据心电信号的特点选择了小波分析理论对心电信号进行处理,完成异常信号的提取。
在用小波对信号进行去噪的过程中,本文选用了二次样条小波作为小波基函数,对三种主要噪声本文采用一种改进的软、硬阈值折中的方法去处肌电干扰和工频干扰,经过仿真实验,效果良好。
在心电信号的特征提取中,本文分别检测出P波、T波和QRS波的起点终点,并分别做了仿真,效果理想。特别是对R波峰值的的检测,本文采用了R波快速检测方法,R波峰值的检测效果明显提高,对于S-T波特征参数的提取利用J+X法检测特征点,为后期疾病的诊断提供重要的依据。
最后本文对远程心电系统进行了设计。选择了无线通信来实现远程心电监控系统,然后以Visual Studio 2005为开发平台,以SQL server 2000为后台数据库,实现了该监控软件。
As living standards improving, the degree of concern for people's health care has also been strengthened in recent years, particularly the demands for medical facilities level are also increasing. The extraction of ECG,which is the main basis for diagnosis of cardiovascular disease, is also the focus of attention at home and abroad in recent years. As the ECG signal is weak and are vulnerable to noise, the extraction becomes more difficult, what is more, long-distance transmission has higher demands for getting rid of the noise.'However, once someone falls ill with heart disease,his life will soon be threatened. As a result, there is necessary to be a system to timely feedback the condition of patients to the hospital, then patients can receive timely treatment. Therefore, it is very meaningful to study remote ECG monitoring system.
This paper mainly uses a wireless communication to achieve long distance transmission of ECG preparing for the diagnosis behind. The system is mainly doing in-depth analysis around the problems such as the ECG Denoising, feature extraction and so on. In addition, according to ECG characteristics the wavelet analysis theory is chosen to process the ECG signal, which is the main task of the system.
In the process of signal denoising by using wavelet, the quadratic spline wavelet is chosen as wavelet basis function. In order to eliminate the three major noise in this paper, such as EMG interference and Power-line interference and so on, an improved compromise of hard and soft threshold method is applied. And the result of the simulation is good.
In the progress of ECG feature extraction, this paper make presentation separately of the detetion of P wave, T wave and the start/end of QRS wave and make simulation separately, the results of which are satisfactory. In particular, to dtecte of wave peak, this paper applies the combination of the wavelet analysis and variable threshold method. The detection effect of the wave peak is improved obviously. As to extract the characteristic parameters of S-T wave, the J+X method is adopted to detect the feature points, to provide important evidence for the late diagnosis of the disease.
Finally, the remote ECG system is designed. The wireless communication is chosen to achieve the long-range ECG monitoring system, which is achieved through Visual Studio 2005, SQL server 2000 as the background database.
引文
[1]TORAL SL. QUERO JM.PEREZ EM.A microprocessor based system for ECG telemedicine and telecare{J]. Materials Research Society Symposium-Proceedings,2001,626(4):526-529.
[2]牟京华.基于PDA的心电远程监护系统研究.[天津大学硕士学位论文].2007：9-11
[3]陈清启,杨庭树,卢喜烈等.心电图学[M].山东科学技术出版社,2002
[4]徐长发,李国宽.实用小波方法.武汉：华中科技大学出版社,2004,1,(2)：107-119.
[5]Valtino X, Afonso, Willis J. Tompkins. et al.Filter bank based ECG beat detection [C]. Proceedings of 19th Annual Conference of IEEE/EMBS.1996,1037-1038.
[6]飞思科技产品研发中心MATLAB6.5辅助小波分析与应用.北京：电了工业出版社(第二版),2003.6：6-12.
[7]邱庆军.QRS波检测方法的回顾与新发展[M].中国医疗器械杂志,1999,23(6)：344-349
[8]G.Barlas and E.Skordalakis. A Novel Family of Compression Algorithms for ECGand Other Semiperiodical, One-dimensional, Biomedical Signals.IEEETrans. Biomed.Eng.1996,43:820-828
[9]魏明果.实用小波分析.北京：北京理工大学出版社,2005：8-13页
[10]Juan Pablo Martinez, et al. A wavelet based ECG delineator evaluation on standard databases. IEEE Trans BME.2004,51(4):570-581.
[11]梁学章,何甲兴,王新民等.小波分析.北京：国防工业出版社,2005.1：46-59.
[12]吴正国,夏立,尹为民.现代信号处理技术：高阶谱,时频分析与小波分析[M].武汉,武汉大学出版社,2003
[13]陈春晓,刘建业.基于小波变换的心电信号去噪处理[J].生物医学工程杂志.2004,21(1)：89-92
[14]薛涛,和卫星.小波阈值去噪技术在ECG信号处理中的应用[J].微计算机信息(测控自动化),2007,23(2-])：306-307
[15]Hsiao-Hsuan Chou, Ying-Jui Chen, Yu-Chien Shiau, Te-son Kuo. An Effective and Efficient Compression Algorithm for ECG Signals with Irregular Periods. Biomedical Engineering, IEEE Trans.2006:1198-1205
[16]孙光耀,余晨生.基于小波变换的心电信号检测的应用研究.山东生物医学工程.2003,22(2)：11.
[17]孙延奎.小波分析及其应用.北京：机械工业出版社,2005,3：221-243.
[18]黄敏,陈海燕,王益民,籍涛.基于小波变换的QRS波形检测方法[[J].哈尔滨理工大学学报,2006,11(4)：81-84
[19]诸强,王学民,胡宾等.基于嵌入式系统的心电无线远程传输系统.天津大学学报,2005(1)：52-56
[20]吴波.基于小波变换的波形特征抽取与识别.华中理工大学学报.1993.
[21]胡广书.现代信号处理教程.北京：清华大学出版社,2004：397-405页
[22]梅志红,杨万铨MATLAB程序设计基础及其应用.北京：清华大学出版社,2005,7：33-58.
[23]朱洪俊.心电信号QRS波群的小波精确识别算法[[J].西南科技大学学报,2007,22(2)：33-39
[24]彭玉华.一种改进的小波变换阈值去噪方法.通信学报.2004,25(8)：119-123页
[25]Li XY, Wang T, et al. ST-T complex automatic analysis of the electrocardiogram signals based on wavelet transform. Bioengineering Conference.2003,144:22-23.
[26]张小飞,徐大专,齐泽锋.基于模极大值小波域的去噪算法研究.数据采集与处理,2003.9,18(3)：315-318
[27]宋喜国,邓亲恺MIT-BIH心率失常数据库的识读及应用[J].中国医学物理学杂志,2004,21(4)：230-232
[28]朱伟芳,王文斌.一种适用于Holter系统的ST段分析方案.生物医学工程学杂志.2004,21(6)：943.
[29][美]Emanuel Stein编著,王瑾,姜铁民译.心律失常快速分析.天津科技翻译出版公司,2004,1：101-144.
[30]周伟MATLAB小波分析高级技术.西安电子科技大学出版社,2006,1：105-111.
[31]L. Shi. Fault Diagnosis of Pneumatic Actuator Using Adaptive Network-Based Fuzzy Inference System Models and a Learning Vector Quantization Neural Network. Proceeding of Neural Networks and FUZZ-IEEE 2004 International Conference.
[32]Tom. M, Mitchell. Machine Learning机械工业出版社,2003,38-48.
[33]Senhadji. WaVelet analysis of ECG signal. EEE-EMBS,1990,12(2):811.
[34]诸强,王学民,胡宾等.无线远程医疗系统.北京生物医学工程,2004(3)：225-227
[35]李翠微,郑崇勋.ECG自动分析技术的发展[[J].国外生物医学工程分册,199417(1)：15-21
[36]苏秦,赵鹤鸣.基于小波变换的多尺度多i值语音信号去噪[J].苏州大学学报(工科版),2003,23(6)：18-22
[37]李翠微等.ECG信号的小波变换检测方法[[J].中国生物医学工程学报,199514(1)：59-66
[38]Bert-Uwe Kohler, Carsten Henning, Reinhold Orglmeiter. The Principles of Software QRS Detection[J]. IEEE Engineering in Medicine and Biology,2002:Jan/Feb:42-57
[39]M. Veterli. J. Kovacevic. Wavelets and Sub band Coding. Englewood Clifs,.NJ: Prentice-Hall.1995.
[40]L. Sommo, O.Pahlnn, M.Nygards. A QRS Detection, A Study of Performance. IEEE Trans.Biomed Eng,1985,32(6):392-401.
[41]P.Parker. Analysis of EEG transients by means of matching pursuit. Annals Biomedical Engineering.1998,23:608-611.
[42]祝海龙,郭天佑,屈梁生.基于二进小波变换和软阈值改进的信号消噪.自动化学报.2004,30(2)：199-206
[43]陈文菊,潘敏等.利用Hilbert变换实现对QRS波的检测.2003年中国生物医学电子学学术年会,2003：34-35
[44]Qin Shu-ren, Yang Chang-qi, Tang Bao-ping et al. The denoise based on translation invariance wavelet transform and its applications. A Conference on Structural Dynamics, Los Angeles.2002,1:783-787
[45]Valtino X, Afonso, Willis J. Tompkins. etal. Filter bank based ECG beat detection [C]. Proceedings of 19th Annual Conference of IEEE/EMBS.1996,1037-1038.
[46]罗小刚.心电信号处理和特征信息提取方法的研究及心电工作站的研制：[博士研究生学位论文].重庆：重庆大学,2003.3
[47]吴平.LCD便携式急救心电监护仪.[浙江大学硕士学位论文]2002：9-11
[48]苏丽,赵国良,李东明.心电信号QRS波群检测算法研究.哈尔滨工程大学学报.2005,26(4)：513-517页
[2]牟京华.基于PDA的心电远程监护系统研究.[天津大学硕士学位论文].2007：9-11
[3]陈清启,杨庭树,卢喜烈等.心电图学[M].山东科学技术出版社,2002
[4]徐长发,李国宽.实用小波方法.武汉：华中科技大学出版社,2004,1,(2)：107-119.
[5]Valtino X, Afonso, Willis J. Tompkins. et al.Filter bank based ECG beat detection [C]. Proceedings of 19th Annual Conference of IEEE/EMBS.1996,1037-1038.
[6]飞思科技产品研发中心MATLAB6.5辅助小波分析与应用.北京：电了工业出版社(第二版),2003.6：6-12.
[7]邱庆军.QRS波检测方法的回顾与新发展[M].中国医疗器械杂志,1999,23(6)：344-349
[8]G.Barlas and E.Skordalakis. A Novel Family of Compression Algorithms for ECGand Other Semiperiodical, One-dimensional, Biomedical Signals.IEEETrans. Biomed.Eng.1996,43:820-828
[9]魏明果.实用小波分析.北京：北京理工大学出版社,2005：8-13页
[10]Juan Pablo Martinez, et al. A wavelet based ECG delineator evaluation on standard databases. IEEE Trans BME.2004,51(4):570-581.
[11]梁学章,何甲兴,王新民等.小波分析.北京：国防工业出版社,2005.1：46-59.
[12]吴正国,夏立,尹为民.现代信号处理技术：高阶谱,时频分析与小波分析[M].武汉,武汉大学出版社,2003
[13]陈春晓,刘建业.基于小波变换的心电信号去噪处理[J].生物医学工程杂志.2004,21(1)：89-92
[14]薛涛,和卫星.小波阈值去噪技术在ECG信号处理中的应用[J].微计算机信息(测控自动化),2007,23(2-])：306-307
[15]Hsiao-Hsuan Chou, Ying-Jui Chen, Yu-Chien Shiau, Te-son Kuo. An Effective and Efficient Compression Algorithm for ECG Signals with Irregular Periods. Biomedical Engineering, IEEE Trans.2006:1198-1205
[16]孙光耀,余晨生.基于小波变换的心电信号检测的应用研究.山东生物医学工程.2003,22(2)：11.
[17]孙延奎.小波分析及其应用.北京：机械工业出版社,2005,3：221-243.
[18]黄敏,陈海燕,王益民,籍涛.基于小波变换的QRS波形检测方法[[J].哈尔滨理工大学学报,2006,11(4)：81-84
[19]诸强,王学民,胡宾等.基于嵌入式系统的心电无线远程传输系统.天津大学学报,2005(1)：52-56
[20]吴波.基于小波变换的波形特征抽取与识别.华中理工大学学报.1993.
[21]胡广书.现代信号处理教程.北京：清华大学出版社,2004：397-405页
[22]梅志红,杨万铨MATLAB程序设计基础及其应用.北京：清华大学出版社,2005,7：33-58.
[23]朱洪俊.心电信号QRS波群的小波精确识别算法[[J].西南科技大学学报,2007,22(2)：33-39
[24]彭玉华.一种改进的小波变换阈值去噪方法.通信学报.2004,25(8)：119-123页
[25]Li XY, Wang T, et al. ST-T complex automatic analysis of the electrocardiogram signals based on wavelet transform. Bioengineering Conference.2003,144:22-23.
[26]张小飞,徐大专,齐泽锋.基于模极大值小波域的去噪算法研究.数据采集与处理,2003.9,18(3)：315-318
[27]宋喜国,邓亲恺MIT-BIH心率失常数据库的识读及应用[J].中国医学物理学杂志,2004,21(4)：230-232
[28]朱伟芳,王文斌.一种适用于Holter系统的ST段分析方案.生物医学工程学杂志.2004,21(6)：943.
[29][美]Emanuel Stein编著,王瑾,姜铁民译.心律失常快速分析.天津科技翻译出版公司,2004,1：101-144.
[30]周伟MATLAB小波分析高级技术.西安电子科技大学出版社,2006,1：105-111.
[31]L. Shi. Fault Diagnosis of Pneumatic Actuator Using Adaptive Network-Based Fuzzy Inference System Models and a Learning Vector Quantization Neural Network. Proceeding of Neural Networks and FUZZ-IEEE 2004 International Conference.
[32]Tom. M, Mitchell. Machine Learning机械工业出版社,2003,38-48.
[33]Senhadji. WaVelet analysis of ECG signal. EEE-EMBS,1990,12(2):811.
[34]诸强,王学民,胡宾等.无线远程医疗系统.北京生物医学工程,2004(3)：225-227
[35]李翠微,郑崇勋.ECG自动分析技术的发展[[J].国外生物医学工程分册,199417(1)：15-21
[36]苏秦,赵鹤鸣.基于小波变换的多尺度多i值语音信号去噪[J].苏州大学学报(工科版),2003,23(6)：18-22
[37]李翠微等.ECG信号的小波变换检测方法[[J].中国生物医学工程学报,199514(1)：59-66
[38]Bert-Uwe Kohler, Carsten Henning, Reinhold Orglmeiter. The Principles of Software QRS Detection[J]. IEEE Engineering in Medicine and Biology,2002:Jan/Feb:42-57
[39]M. Veterli. J. Kovacevic. Wavelets and Sub band Coding. Englewood Clifs,.NJ: Prentice-Hall.1995.
[40]L. Sommo, O.Pahlnn, M.Nygards. A QRS Detection, A Study of Performance. IEEE Trans.Biomed Eng,1985,32(6):392-401.
[41]P.Parker. Analysis of EEG transients by means of matching pursuit. Annals Biomedical Engineering.1998,23:608-611.
[42]祝海龙,郭天佑,屈梁生.基于二进小波变换和软阈值改进的信号消噪.自动化学报.2004,30(2)：199-206
[43]陈文菊,潘敏等.利用Hilbert变换实现对QRS波的检测.2003年中国生物医学电子学学术年会,2003：34-35
[44]Qin Shu-ren, Yang Chang-qi, Tang Bao-ping et al. The denoise based on translation invariance wavelet transform and its applications. A Conference on Structural Dynamics, Los Angeles.2002,1:783-787
[45]Valtino X, Afonso, Willis J. Tompkins. etal. Filter bank based ECG beat detection [C]. Proceedings of 19th Annual Conference of IEEE/EMBS.1996,1037-1038.
[46]罗小刚.心电信号处理和特征信息提取方法的研究及心电工作站的研制：[博士研究生学位论文].重庆：重庆大学,2003.3
[47]吴平.LCD便携式急救心电监护仪.[浙江大学硕士学位论文]2002：9-11
[48]苏丽,赵国良,李东明.心电信号QRS波群检测算法研究.哈尔滨工程大学学报.2005,26(4)：513-517页
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