基于社区监护的心电信号实时自动分析研究
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摘要
社区医疗服务作为一种新型的医疗模式，已引起国内外的高度重视，具有广阔的发展前景。这种新型的医疗模式对心电监护中心电信号的自动分析也提出了新的要求。本课题的研究正是基于当前社区医疗发展的需求，利用小波变换的方法，对适合于社区监护的心电信号实时自动分析算法进行了研究。
在过去的几十年间，人们借鉴了种种信号处理方法和模式识别技术去分析、识别心电波形，使心电分析与检测的水平得到了很大的提高。但是，至今仍没有一种方法能让临床对其完全满意，而且目前的大多数算法都是基于PC的后处理算法，无法满足社区监护中的实时要求。同时，由于社区生理监护的特殊性，对自动分析的需求更为迫切，特别是对危及病人性命的心电特征的及时识别对病人有着极其重要的意义。
目前报道的基本算法大都是把重点放在QRS波群的检测上，而对比较难于检测的ST-T段的研究比较少，恰恰对该段的分析在社区心血管系统危重病人的监护中有着重要的意义。本研究利用小波变换方法，首先对采集的信号进行预处理，滤除工频干扰、肌电干扰和基线漂移等噪声，然后对信号的QRS波群进行特征识别，识别出R波等特征点，再以此为基准点对ST-T段进行了识别研究，提出了适合于ST-T段识别的有效算法。
本研究分别采用了国际公认的MIT-BIH心电数据库的数据以及心电模拟器数据对所提出的算法进行了大量的实验研究，验证了算法的精确性和有效性。
在本研究中提出的ST-T段的分析方法在国内外尚未见到报道。
The community medicine has attracted very much attention as a new medicine mode in the world and has very good foreground . This new medicine mode has brought forward new demand to the automatic analysis of ECG signals. In this study, I have used the way of wavelet transform (WT) to study the real-time automatic analysis of ECG signals.
In the past several ten years, many people has made great efforts to detect and analysis the ECG waves with vary methods. But none of those methods could satisfy the clinical demand, and the most of those algorithms are after-analysis algorithm based on PC, it cann't satisfy the demand of community-monitor. In the same time, the community-monitor requires the automatic analysis technology much more because of the particularity that community-monitor differs from the in-hospital-monitor. It is very important for the patient who has disease that can endanger the patient's life.
The existent algorithms often put the importance on the detection of QRS complex, there is few researcher to study the detection of ST-T segment. The analysis of ST-T segment is more important actually to community-monitor. In this study, I adopted the way of wavelet transform to process ECG signals. First of all, the algorithm based on WT eliminated the three primary noise (including power line interference, electrical interference of muscle and baseline wander), and then detected the character of QRS complex. In the end, I study the way of ST-T segment detection based on the QRS character and brought forward a algorithm for the ST-T detection.
In this study, a lot of experiments with the data from MIT-BIH database and ECG simulator validated the accuracy and feasibility of the proposed algorithm.
The detection algorithm of ST-T segment is a novel method ,there is no same method reported in the world up to now.
在过去的几十年间，人们借鉴了种种信号处理方法和模式识别技术去分析、识别心电波形，使心电分析与检测的水平得到了很大的提高。但是，至今仍没有一种方法能让临床对其完全满意，而且目前的大多数算法都是基于PC的后处理算法，无法满足社区监护中的实时要求。同时，由于社区生理监护的特殊性，对自动分析的需求更为迫切，特别是对危及病人性命的心电特征的及时识别对病人有着极其重要的意义。
目前报道的基本算法大都是把重点放在QRS波群的检测上，而对比较难于检测的ST-T段的研究比较少，恰恰对该段的分析在社区心血管系统危重病人的监护中有着重要的意义。本研究利用小波变换方法，首先对采集的信号进行预处理，滤除工频干扰、肌电干扰和基线漂移等噪声，然后对信号的QRS波群进行特征识别，识别出R波等特征点，再以此为基准点对ST-T段进行了识别研究，提出了适合于ST-T段识别的有效算法。
本研究分别采用了国际公认的MIT-BIH心电数据库的数据以及心电模拟器数据对所提出的算法进行了大量的实验研究，验证了算法的精确性和有效性。
在本研究中提出的ST-T段的分析方法在国内外尚未见到报道。
The community medicine has attracted very much attention as a new medicine mode in the world and has very good foreground . This new medicine mode has brought forward new demand to the automatic analysis of ECG signals. In this study, I have used the way of wavelet transform (WT) to study the real-time automatic analysis of ECG signals.
In the past several ten years, many people has made great efforts to detect and analysis the ECG waves with vary methods. But none of those methods could satisfy the clinical demand, and the most of those algorithms are after-analysis algorithm based on PC, it cann't satisfy the demand of community-monitor. In the same time, the community-monitor requires the automatic analysis technology much more because of the particularity that community-monitor differs from the in-hospital-monitor. It is very important for the patient who has disease that can endanger the patient's life.
The existent algorithms often put the importance on the detection of QRS complex, there is few researcher to study the detection of ST-T segment. The analysis of ST-T segment is more important actually to community-monitor. In this study, I adopted the way of wavelet transform to process ECG signals. First of all, the algorithm based on WT eliminated the three primary noise (including power line interference, electrical interference of muscle and baseline wander), and then detected the character of QRS complex. In the end, I study the way of ST-T segment detection based on the QRS character and brought forward a algorithm for the ST-T detection.
In this study, a lot of experiments with the data from MIT-BIH database and ECG simulator validated the accuracy and feasibility of the proposed algorithm.
The detection algorithm of ST-T segment is a novel method ,there is no same method reported in the world up to now.
引文
[1] 杨福生等．生物医学信号的处理和识别．天津科技翻译出版公司，1997
[2] 杨福生．小波变换的工程分析与应用．科学出版社，1999
[3] 秦前清，杨宗凯．实用小波分析．西安电子科技大学出版社，1994
[4] 胡广书．数字信号处理—理论、算法与实现．清华大学出版社，1997
[5] 胡昌华，等．基于MATLAB的系统分析与设计—小波分析．西安电子科技大学出版社，1999
[6] 邹鲲，等．MATLAB 6.x信号处理．清华大学出版社，2002
[7] 周玉珍．简明心电图手册．人民军医出版社，2002
[8] 邹波，柴继红．远程心电监护系统的研制．深圳职业技术学院学报，2003，3，1
[9] 郑燕，梁美容．面向社区医疗服务网络的胎儿监护系统．现代医学仪器与应用，2003，12(1)，20
[10] 杨杰，等．家庭远程医疗监护报警和咨询智能系统．高技术通讯，2002，6，4
[11] 王元元．基于移动计算设备的远程监护系统设计．中国新医药，2003，2(9)，58
[12] 张更生．心电信号的计算机分析——特征检测．生物学杂志，1997，76，18-22．
[13] 戚建新，等．基于微机的心电信号实时自动分析系统．北京生物医学工程，1997，16(3)，157
[14] 赵捷，华玫．滤除基线漂移和工频干扰的简单数字滤波器．山东生物医学工程，1995，3-4，26
[15] 陆英北，等．基于小波变换的心电信号基线矫正方法．北京生物医学工程，2000，19(4)，235
[16] 李刚，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
[17] 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，2002，31(2)，50
[18] 费保蔚，等．心电信号数字滤波处理方法．中国医疗器械杂志，1996，20(5)，262
[19] 肖咏梅，等．心电自动分析技术．生物医学工程学杂志，2000，17(3)，339
[20] I.K.Daskalov,I.I.Christov. Automatic detection of the electrocardiogram T-wave end. Med. Biol. Eng. Comput., 1999,37,348-353
[21] P.Laguna, etc. Adaptive estimation of QRS complex wave features of ECG sinai by the Hermite model. Med. Biol. Eng. Comput. 1996,1,58-68
[22] Patrick S.Hamilton. A Comparison of Adaptive and Nonadaptive Filters for Reduction of Power Line Interference in the ECG. IEEE TRANS on BME,1996,43(1),105-109
[23] 李翠微，等．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
[24] 姬军，等．心电信号QRS波的识别算法及程序设计．北京生物医学工程，2001，20(2)，119-125
[25] 刘欣桃，等．心电信号的计算机辅助分析方法的研究．计算机辅助设计与图形学学报，1997，9(2)，116-122
[26] 杨慧忠，张菊鹏，白净．一种实时去除心电信号噪声的静息期检测判别方法．北京生物医学工程，1997，16(1)，6
[27] Shubha Kadambe,etc. Wavelet Transform-Based QRS Complex Detector. IEEE TRANS on BME, 1999,46(7),838-848
[28] Cuiwei Li,etc. Detection of ECG Characteristic Points Using Wavelet Transforms. IEEE TRANS on BME,1995,42(1),21-28
[29] Karsten Sternickel. Automatic pattern recognition in ECG time series. Computer Methods and Programs in Biomedicine,2002,68,109-115
[30] 温凌锋，白净，等．QRS波群检测方法的新进展．国外医学生物医学工程分册，2001，24(5)，193
[31] 谢国明，等．用小波变换结合神经网络检测ECG信号的P波．生物医学工程学杂志，1999，16(3)，320
[32] 张凯，等．心电信号自动检测的小波变换方法．扬州大学学报，2001，4(1)，55-57
[33] 范晓东，等．动态心电图ST-T段参数测量方法，生物医学工程学杂志，1997，14(11．51-53
[34] Yuxing Yang,etc. Development of a digital signal processor-based new 12-lead synchronization electrocardiogram automatic analysis system, Computer Methods and Programs in Biomedicine, 2002,69,57-63
[35] David Cuesta-Frau, etc. Clustering of electrocardiagraph signals in computer-aided Holter analysis. Computer Methods and Programs in Biomedicine, 2003,72,179-196
[36] J.S.Sahambi,S.N.Tandon, R.K.Bhatt. Wavelet based ST-segment analysis. Med. Biol. Eng. Comput.. 1998,9,568-572
[37] K.L.Park,etc. Design of a wavelet interpolation filter for enhancement of the
ST-segment. Medical & Biological Engineering & Computing, 2001,39,355-361
[38] Pedro Gomis,etc. Analysis of Abnormal Signals Within the QRS Complex of the High-Resolution Electrocardiogram. IEEE Transactions on Biomedical Engineering, 1997,44(8),681-687
[39] Valtino X,etc. ECG Beat Detection Using Filter Banks. IEEE TRANS on BME, 1999,46(2), 192-202
[40] Emilio Soria-Olivas, etc. Application of Adaptive Signal Processing for Determining the Limits of P and T Waves in an ECG. IEEE TRANS on BME,1998,45(8),1077-1080
[2] 杨福生．小波变换的工程分析与应用．科学出版社，1999
[3] 秦前清，杨宗凯．实用小波分析．西安电子科技大学出版社，1994
[4] 胡广书．数字信号处理—理论、算法与实现．清华大学出版社，1997
[5] 胡昌华，等．基于MATLAB的系统分析与设计—小波分析．西安电子科技大学出版社，1999
[6] 邹鲲，等．MATLAB 6.x信号处理．清华大学出版社，2002
[7] 周玉珍．简明心电图手册．人民军医出版社，2002
[8] 邹波，柴继红．远程心电监护系统的研制．深圳职业技术学院学报，2003，3，1
[9] 郑燕，梁美容．面向社区医疗服务网络的胎儿监护系统．现代医学仪器与应用，2003，12(1)，20
[10] 杨杰，等．家庭远程医疗监护报警和咨询智能系统．高技术通讯，2002，6，4
[11] 王元元．基于移动计算设备的远程监护系统设计．中国新医药，2003，2(9)，58
[12] 张更生．心电信号的计算机分析——特征检测．生物学杂志，1997，76，18-22．
[13] 戚建新，等．基于微机的心电信号实时自动分析系统．北京生物医学工程，1997，16(3)，157
[14] 赵捷，华玫．滤除基线漂移和工频干扰的简单数字滤波器．山东生物医学工程，1995，3-4，26
[15] 陆英北，等．基于小波变换的心电信号基线矫正方法．北京生物医学工程，2000，19(4)，235
[16] 李刚，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
[17] 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，2002，31(2)，50
[18] 费保蔚，等．心电信号数字滤波处理方法．中国医疗器械杂志，1996，20(5)，262
[19] 肖咏梅，等．心电自动分析技术．生物医学工程学杂志，2000，17(3)，339
[20] I.K.Daskalov,I.I.Christov. Automatic detection of the electrocardiogram T-wave end. Med. Biol. Eng. Comput., 1999,37,348-353
[21] P.Laguna, etc. Adaptive estimation of QRS complex wave features of ECG sinai by the Hermite model. Med. Biol. Eng. Comput. 1996,1,58-68
[22] Patrick S.Hamilton. A Comparison of Adaptive and Nonadaptive Filters for Reduction of Power Line Interference in the ECG. IEEE TRANS on BME,1996,43(1),105-109
[23] 李翠微，等．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
[24] 姬军，等．心电信号QRS波的识别算法及程序设计．北京生物医学工程，2001，20(2)，119-125
[25] 刘欣桃，等．心电信号的计算机辅助分析方法的研究．计算机辅助设计与图形学学报，1997，9(2)，116-122
[26] 杨慧忠，张菊鹏，白净．一种实时去除心电信号噪声的静息期检测判别方法．北京生物医学工程，1997，16(1)，6
[27] Shubha Kadambe,etc. Wavelet Transform-Based QRS Complex Detector. IEEE TRANS on BME, 1999,46(7),838-848
[28] Cuiwei Li,etc. Detection of ECG Characteristic Points Using Wavelet Transforms. IEEE TRANS on BME,1995,42(1),21-28
[29] Karsten Sternickel. Automatic pattern recognition in ECG time series. Computer Methods and Programs in Biomedicine,2002,68,109-115
[30] 温凌锋，白净，等．QRS波群检测方法的新进展．国外医学生物医学工程分册，2001，24(5)，193
[31] 谢国明，等．用小波变换结合神经网络检测ECG信号的P波．生物医学工程学杂志，1999，16(3)，320
[32] 张凯，等．心电信号自动检测的小波变换方法．扬州大学学报，2001，4(1)，55-57
[33] 范晓东，等．动态心电图ST-T段参数测量方法，生物医学工程学杂志，1997，14(11．51-53
[34] Yuxing Yang,etc. Development of a digital signal processor-based new 12-lead synchronization electrocardiogram automatic analysis system, Computer Methods and Programs in Biomedicine, 2002,69,57-63
[35] David Cuesta-Frau, etc. Clustering of electrocardiagraph signals in computer-aided Holter analysis. Computer Methods and Programs in Biomedicine, 2003,72,179-196
[36] J.S.Sahambi,S.N.Tandon, R.K.Bhatt. Wavelet based ST-segment analysis. Med. Biol. Eng. Comput.. 1998,9,568-572
[37] K.L.Park,etc. Design of a wavelet interpolation filter for enhancement of the
ST-segment. Medical & Biological Engineering & Computing, 2001,39,355-361
[38] Pedro Gomis,etc. Analysis of Abnormal Signals Within the QRS Complex of the High-Resolution Electrocardiogram. IEEE Transactions on Biomedical Engineering, 1997,44(8),681-687
[39] Valtino X,etc. ECG Beat Detection Using Filter Banks. IEEE TRANS on BME, 1999,46(2), 192-202
[40] Emilio Soria-Olivas, etc. Application of Adaptive Signal Processing for Determining the Limits of P and T Waves in an ECG. IEEE TRANS on BME,1998,45(8),1077-1080