心电信号数据提取算法的设计与实现
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摘要
近年来,心血管疾病已经成为威胁人类健康的主要疾病之一。ECG信号能直观反映心律波动状况,成为分析研究的热门领域。本课题是心电信号数据提取算法的设计与实现,与硬件检测相对应,这是实现心电数据检测与提取的软件手段。心电图中的QRs波群包含了心脏状态的重要信息,是进行其它心电参数检测(如P波、T波)的前提,因此QRS波群的准确识别与检测对于心脏疾病的预防和诊断具有重要的意义。
本文对心电数据提取算法的研究是基于一款微型心电仪的应用需求,紧紧围绕心电仪的两种数据处理模式,即单机检测模式与联机数据处理模式展开算法的设计与实现。针对两种模式不同的应用环境与各异的运算存储能力,分别设计并实现了差分阈值法与小波变换法对心电信号中QRS波群的检测与定位。
在差分阈值法的设计中,依据实时处理数据的要求,设计以短时程为单位的数据块处理模式,实现以时程不断更新提取数据的模式。在消除R波误判方面,实现了R波半波宽法的检测,提高了R波的识别准确度。算法在仿真环境中通过了运行检测。
在小波变换法的设计中,运用小波多尺度分析的特征,使用二次微分Marr小波变换对心电数据按照Mallat算法进行R波的检测与提取。运用综合检测方法进行复检,如补偿法,从而在特定尺度上识别QRS波群的同时有效抑制高频噪声,如工频干扰,从而提高了R波检测的正确率。对于P波,T波的检测提出了思路与方法。
本文使用国际上广泛采用的MIT-BIH心电数据作为算法处理的数据与验证算法准确度的标准。通过算法运行处理并将提取的数据结果与MIT-BIH提供的标准信息做比较,对算法的准确度做出客观科学的评价。测试结果表明,本文设计的两种算法准确率较高,能满足实际应用的要求。
In recent years, as one of the major diseases, cardiovascular disease has become a threat to human health. ECG analysis is a hot research area because it can reflect the state of the heart fluctuations intuitively. This thesis is about the study and implementation of the algorithm in detecting characteristic points of ECG signals. Opposite to the hardware detection, it is the method of software in ECG data detection. QRS waves contain important information about the heart condition and it is the prerequisite of detecting the other ECG data such as the P wave and T wave, so accurately identifying the QRS waves make the important significance for heart disease prevention and diagnosis.
The algorithms are based on the application of a Micro ECG Device for the two data processing patterns, one is single instrument detection and the other is detection with PC connection. The two different environments have different computing and storage capacity, so the differential threshold method and wavelet transform method are adapted to the detection and location of QRS waves in two patterns.
In the implementation of the differential threshold method, based on real-time data processing, designed short-range time as a unit of data block mode, to achieve continuously updated the characteristic points. Using the half width of R wave detection method to eliminate the mistake of the R wave detection, the method can improve the R wave detection accuracy.
In the implementation of the wavelet transform method, using the character in multi-scale analysis of wavelet, designed the Mallat algorithm based on dyadic Marr wavelet transform to detect the QRS waves. Using an integrative method to identify the result, it can detect the QRS waves in the specific scale with effective inhibition of high-frequency noise, such as power-line interference, the method can improve the R wave detection accuracy. The P wave and T wave detection ideas and methods are proposed.
This thesis introduced the widely used MIT-BIH ECG data as the object of processing algorithms and standard to measure the accuracy of the algorithm. Making objective scientific evaluation with the algorithm by checking result compared with the standard annotation of MIT-BIH. Test result shows that the two designed algorithms can meet the practical applications with high accuracy.
本文对心电数据提取算法的研究是基于一款微型心电仪的应用需求,紧紧围绕心电仪的两种数据处理模式,即单机检测模式与联机数据处理模式展开算法的设计与实现。针对两种模式不同的应用环境与各异的运算存储能力,分别设计并实现了差分阈值法与小波变换法对心电信号中QRS波群的检测与定位。
在差分阈值法的设计中,依据实时处理数据的要求,设计以短时程为单位的数据块处理模式,实现以时程不断更新提取数据的模式。在消除R波误判方面,实现了R波半波宽法的检测,提高了R波的识别准确度。算法在仿真环境中通过了运行检测。
在小波变换法的设计中,运用小波多尺度分析的特征,使用二次微分Marr小波变换对心电数据按照Mallat算法进行R波的检测与提取。运用综合检测方法进行复检,如补偿法,从而在特定尺度上识别QRS波群的同时有效抑制高频噪声,如工频干扰,从而提高了R波检测的正确率。对于P波,T波的检测提出了思路与方法。
本文使用国际上广泛采用的MIT-BIH心电数据作为算法处理的数据与验证算法准确度的标准。通过算法运行处理并将提取的数据结果与MIT-BIH提供的标准信息做比较,对算法的准确度做出客观科学的评价。测试结果表明,本文设计的两种算法准确率较高,能满足实际应用的要求。
In recent years, as one of the major diseases, cardiovascular disease has become a threat to human health. ECG analysis is a hot research area because it can reflect the state of the heart fluctuations intuitively. This thesis is about the study and implementation of the algorithm in detecting characteristic points of ECG signals. Opposite to the hardware detection, it is the method of software in ECG data detection. QRS waves contain important information about the heart condition and it is the prerequisite of detecting the other ECG data such as the P wave and T wave, so accurately identifying the QRS waves make the important significance for heart disease prevention and diagnosis.
The algorithms are based on the application of a Micro ECG Device for the two data processing patterns, one is single instrument detection and the other is detection with PC connection. The two different environments have different computing and storage capacity, so the differential threshold method and wavelet transform method are adapted to the detection and location of QRS waves in two patterns.
In the implementation of the differential threshold method, based on real-time data processing, designed short-range time as a unit of data block mode, to achieve continuously updated the characteristic points. Using the half width of R wave detection method to eliminate the mistake of the R wave detection, the method can improve the R wave detection accuracy.
In the implementation of the wavelet transform method, using the character in multi-scale analysis of wavelet, designed the Mallat algorithm based on dyadic Marr wavelet transform to detect the QRS waves. Using an integrative method to identify the result, it can detect the QRS waves in the specific scale with effective inhibition of high-frequency noise, such as power-line interference, the method can improve the R wave detection accuracy. The P wave and T wave detection ideas and methods are proposed.
This thesis introduced the widely used MIT-BIH ECG data as the object of processing algorithms and standard to measure the accuracy of the algorithm. Making objective scientific evaluation with the algorithm by checking result compared with the standard annotation of MIT-BIH. Test result shows that the two designed algorithms can meet the practical applications with high accuracy.
引文
[1]willis J. Tommpkins,林家瑞,邦荃.生物医学数字信号处理[M].武汉：华中科技大学出版社,2001
[2]郭继鸿,张萍.动态心电图学[M].北京：人民卫生出版社,2003
[3]纪承寅,陈忠良,吕军.典型心电图图谱[M].北京：科学技术文献出版社,2007
[4]张开滋,刘海洋,吴杰.心电信息学[M].北京：北京技术文献出版社,1998
[5]李翠微,郑崇勋.ECG自动分析技术的发展[J],国外医学生物医学工程分册,1994,17卷第1期
[6]范晓东等.国外动态ECG监护技术的研究现状[J].国外医学生物医学工程分册,1990
[7]张红煊,朱贻盛,牛金海,童善保.异常心电节律VT和VF信号的复杂性分析[J],物理学报2000,49(8)
[8]J Fraden al. QRS wave detection [J]. Medical & Biological Engineering & Computing,1980
[9]朱凌云,吴宝明,王正国,李刚,曹长修,朱新建,闫庆广.移动心电监护系统QRS波的实时检测算法研究[J].仪器仪表学报2005,26(6)
[10]PAN J, TOMPKINS W. J. A Real-Time QRS Detection Algorithm[J]. IEEE Trans Biomed Eng,1985,32:230-236
[11]Hamilton PS,Tompkins WJ. Quantitative Invertigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Databse[J]. IEEE Trans BME,1986
[12]李延军,严洪.QRS波群检测常用算法的比较[J].生物医学工程学进展,2008,29卷第2期
[13]蔡志勇,张徐娟,张华,吴建华.小波变换Mallat算法的ADSP21160实现[J].微型电子学与计算机,2003,第10期
[14]崔晓琳.12导联心电信号同步采集系统[D],北京交通大学,2008.6
[15]MIT-BIH Arrhythmia Database, http://www.physionet.org/physiobank/database/mitdb/
[16]周洪利.计算机高级接口技术[M].北京：清华大学出版社,北京交通大学出版社,2008
[17]Silicon Laboratories C8051F120 MCU Data Sheet,2007
[18]黄宛.临床心电图学(第五版)[M].北京：人民卫生出版社,1998
[19]王磊,郑重勋,叶继伦,刘峰.一种高效的QRS波实时检测方法[J].北京生物医学工程,1998,第17卷第4期
[20]闫润强,詹永麒,胡伟国,张永红.12导联同步心电信号自动检测技术的研究[J].中国医疗器械杂志,2002,第26卷第02期
[21]朱泽煌.MIT-BIH心电数据库的开发及用作检测标准[J].中国生物医学工程学报,1993,12(4)
[22]邹波,周远.一种滤除基线漂移和工频干扰的数字滤波算法[J].深圳职业技术学院学报,2005,第4期
[22]杨福生.小波变换工程分析与应用[M].北京：科学出版社,1999
[24]季虎,毛玲,孙即祥.基于小波变换与形态学运算的R波检测算法[J].计算机应用,2006,第26卷第5期
[25]成礼智,郭汉伟.小波与离散变换理论及工程实践[M].北京：清华大学出版社,2005
[26]彭玉华.小波变换与工程应用[M].北京：科学出版社,1999
[27]徐佩霞,孙功宪.小波分析与实用实例[M].合肥：中国科技大学出版社,1996
[28]唐向宏,李齐良.时频分析与小波变换[M].北京：科学出版社,2008
[29]Mallat S. Singular detection and processing with wavelet[J]. IEEEE Trans Information Theroy 1992,38(2) 617-643
[30]王晓玲,朱坚民,郭冰菁.基于小波变换的心电图QRS波群检测方法[J].河南科技大学学报,2006,第27卷第4期
[31]王恒迪,朱坚民,梁丽,沈波.一种简单准确的R波检测算法[J].生物医学工程学,2007,24(5)1019-1022
[32]王超文,吕杨生,黄玉玺.用ECG信号检测与重建的双正交样条小波滤波器[J].北京生物医学工程,2001.3
[2]郭继鸿,张萍.动态心电图学[M].北京：人民卫生出版社,2003
[3]纪承寅,陈忠良,吕军.典型心电图图谱[M].北京：科学技术文献出版社,2007
[4]张开滋,刘海洋,吴杰.心电信息学[M].北京：北京技术文献出版社,1998
[5]李翠微,郑崇勋.ECG自动分析技术的发展[J],国外医学生物医学工程分册,1994,17卷第1期
[6]范晓东等.国外动态ECG监护技术的研究现状[J].国外医学生物医学工程分册,1990
[7]张红煊,朱贻盛,牛金海,童善保.异常心电节律VT和VF信号的复杂性分析[J],物理学报2000,49(8)
[8]J Fraden al. QRS wave detection [J]. Medical & Biological Engineering & Computing,1980
[9]朱凌云,吴宝明,王正国,李刚,曹长修,朱新建,闫庆广.移动心电监护系统QRS波的实时检测算法研究[J].仪器仪表学报2005,26(6)
[10]PAN J, TOMPKINS W. J. A Real-Time QRS Detection Algorithm[J]. IEEE Trans Biomed Eng,1985,32:230-236
[11]Hamilton PS,Tompkins WJ. Quantitative Invertigation of QRS Detection Rules Using the MIT/BIH Arrhythmia Databse[J]. IEEE Trans BME,1986
[12]李延军,严洪.QRS波群检测常用算法的比较[J].生物医学工程学进展,2008,29卷第2期
[13]蔡志勇,张徐娟,张华,吴建华.小波变换Mallat算法的ADSP21160实现[J].微型电子学与计算机,2003,第10期
[14]崔晓琳.12导联心电信号同步采集系统[D],北京交通大学,2008.6
[15]MIT-BIH Arrhythmia Database, http://www.physionet.org/physiobank/database/mitdb/
[16]周洪利.计算机高级接口技术[M].北京：清华大学出版社,北京交通大学出版社,2008
[17]Silicon Laboratories C8051F120 MCU Data Sheet,2007
[18]黄宛.临床心电图学(第五版)[M].北京：人民卫生出版社,1998
[19]王磊,郑重勋,叶继伦,刘峰.一种高效的QRS波实时检测方法[J].北京生物医学工程,1998,第17卷第4期
[20]闫润强,詹永麒,胡伟国,张永红.12导联同步心电信号自动检测技术的研究[J].中国医疗器械杂志,2002,第26卷第02期
[21]朱泽煌.MIT-BIH心电数据库的开发及用作检测标准[J].中国生物医学工程学报,1993,12(4)
[22]邹波,周远.一种滤除基线漂移和工频干扰的数字滤波算法[J].深圳职业技术学院学报,2005,第4期
[22]杨福生.小波变换工程分析与应用[M].北京：科学出版社,1999
[24]季虎,毛玲,孙即祥.基于小波变换与形态学运算的R波检测算法[J].计算机应用,2006,第26卷第5期
[25]成礼智,郭汉伟.小波与离散变换理论及工程实践[M].北京：清华大学出版社,2005
[26]彭玉华.小波变换与工程应用[M].北京：科学出版社,1999
[27]徐佩霞,孙功宪.小波分析与实用实例[M].合肥：中国科技大学出版社,1996
[28]唐向宏,李齐良.时频分析与小波变换[M].北京：科学出版社,2008
[29]Mallat S. Singular detection and processing with wavelet[J]. IEEEE Trans Information Theroy 1992,38(2) 617-643
[30]王晓玲,朱坚民,郭冰菁.基于小波变换的心电图QRS波群检测方法[J].河南科技大学学报,2006,第27卷第4期
[31]王恒迪,朱坚民,梁丽,沈波.一种简单准确的R波检测算法[J].生物医学工程学,2007,24(5)1019-1022
[32]王超文,吕杨生,黄玉玺.用ECG信号检测与重建的双正交样条小波滤波器[J].北京生物医学工程,2001.3