心电信号R波与P波检测研究
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摘要
随着信号处理技术的发展,每一种新的信号处理技术都将很快应用到医学信号处理,并促使医学信号处理技术的革新。携带人体机能信息的生理信号总是通过复杂的模式混和在一起,而临床医师希望采用无创的、方便的方法获取生理信息,分析产生生理信号的信号源的功能状态,作出诊断结论。因此,关于心电信号预处理,波形检测以及各信号源信号提取的研究一直为各方所关注。随有计算机技术的进步,所采用的方法也在不断的改进中。
本文首先简要综述了心电信号预处理,R波检测处理及PCA房颤信号提取与P波时限测量研究。
然后研究了基于Hilbert变换去噪算法。分析了它在不同方法进行重构时的去噪效果。实验对比显示:不同的重构导致滤波后信号的信噪比是有差异的。该算法能够有效滤去工频干扰等高频噪声以及基线漂移等常见噪声。
接着本文研究了心电特征信息提取方法。在对目前较成熟运用于心电特征波识别算法进行分析后,对Hilbert变换法与差分阈值R波检测方法进行了研究,在此基础上创新性地提出了基于Hilbert变换联合差分阈值检测算法来识别和定位心电R波。通过大量的实验,论证了本算法的准确性和可靠性。
本文重点研究心电信号P波的检测。首先分析了从体表心电图估计患者病理活动状态,需进行信号提取,所以基于主分量分析(PCA)研究,实现了一种从患者的单导联体表心电图中提取房颤信号的新方法。通过对Physionet数据库和MIT-BIH数据库中的大量数据测试和验证,获得了一种鲁棒、效果好的单导联房颤信号提取技术,但该方法对于P波分离不成功,本文通过Hilbert变换法与差分阈值R波检测方法确定R波位置后提取每心搏的前2/5个周期进行P波分析。得出了一种可供临床参考的P波时限测量方法。
With the signal processing technology development, each kind of new signal processing techniques apply to process the medical signal very quickly and make the technology of medical signal processing to innovate. The physiology signals which take the functional information of human always mix with a complicated mode. But doctors hope to analyze the function state of the sources which create physiological signal and make a diagnosis by using unhurt and convenient ways to obtain the physiology information. Therefore, the research of the electrocardiogram( ECG) processing, like the wave detection and signal source extraction has been paid attention. With the compute technology developing, the methods adopted are also in the continuous improvement.
The paper firstly discussed the ECG de-noising, the examination of R wave, atrial fibrillation signal drawing with PCA and the time measuring of the P wave.
Then the ECG de-noising by using the Hilbert transformation is discussed. The paper analyzes the different result with restructuring differently. The contrasting experiments show that the Signal-to-Noise rate is different through the ECG de-noising methods with different restructured. This way can effectively remove of the interference, such as baseline moving and power line interference etc.
The method for extraction of the ECG characteristic information is discussed as follow. After analyzing current methods of ECG extraction, we take a research of R wave detection on Hilbert transformation and difference threshold method. we found that it is a great way to detect R wave uniting the two methods. A great deal of experiments proved that accuracy and credibility of the way is higher.
The emphasis of the paper is to examine P wave. We should do ECG extraction if want to estimate the pathologic state of sufferer from ECG. According to principal component analysis (PCA) research, a new method to draw the atrial fibrillation signal is adopted. Through a great deal of data test from the Physionet database, a technique extraction of atrial fibrillation signal is effectively but is not successful to separate the P wave. P waves drawing from each before 2/5 periods of heart are carried on the analysis. We get a method to measure the P wave time that it can help the doctors.
本文首先简要综述了心电信号预处理,R波检测处理及PCA房颤信号提取与P波时限测量研究。
然后研究了基于Hilbert变换去噪算法。分析了它在不同方法进行重构时的去噪效果。实验对比显示:不同的重构导致滤波后信号的信噪比是有差异的。该算法能够有效滤去工频干扰等高频噪声以及基线漂移等常见噪声。
接着本文研究了心电特征信息提取方法。在对目前较成熟运用于心电特征波识别算法进行分析后,对Hilbert变换法与差分阈值R波检测方法进行了研究,在此基础上创新性地提出了基于Hilbert变换联合差分阈值检测算法来识别和定位心电R波。通过大量的实验,论证了本算法的准确性和可靠性。
本文重点研究心电信号P波的检测。首先分析了从体表心电图估计患者病理活动状态,需进行信号提取,所以基于主分量分析(PCA)研究,实现了一种从患者的单导联体表心电图中提取房颤信号的新方法。通过对Physionet数据库和MIT-BIH数据库中的大量数据测试和验证,获得了一种鲁棒、效果好的单导联房颤信号提取技术,但该方法对于P波分离不成功,本文通过Hilbert变换法与差分阈值R波检测方法确定R波位置后提取每心搏的前2/5个周期进行P波分析。得出了一种可供临床参考的P波时限测量方法。
With the signal processing technology development, each kind of new signal processing techniques apply to process the medical signal very quickly and make the technology of medical signal processing to innovate. The physiology signals which take the functional information of human always mix with a complicated mode. But doctors hope to analyze the function state of the sources which create physiological signal and make a diagnosis by using unhurt and convenient ways to obtain the physiology information. Therefore, the research of the electrocardiogram( ECG) processing, like the wave detection and signal source extraction has been paid attention. With the compute technology developing, the methods adopted are also in the continuous improvement.
The paper firstly discussed the ECG de-noising, the examination of R wave, atrial fibrillation signal drawing with PCA and the time measuring of the P wave.
Then the ECG de-noising by using the Hilbert transformation is discussed. The paper analyzes the different result with restructuring differently. The contrasting experiments show that the Signal-to-Noise rate is different through the ECG de-noising methods with different restructured. This way can effectively remove of the interference, such as baseline moving and power line interference etc.
The method for extraction of the ECG characteristic information is discussed as follow. After analyzing current methods of ECG extraction, we take a research of R wave detection on Hilbert transformation and difference threshold method. we found that it is a great way to detect R wave uniting the two methods. A great deal of experiments proved that accuracy and credibility of the way is higher.
The emphasis of the paper is to examine P wave. We should do ECG extraction if want to estimate the pathologic state of sufferer from ECG. According to principal component analysis (PCA) research, a new method to draw the atrial fibrillation signal is adopted. Through a great deal of data test from the Physionet database, a technique extraction of atrial fibrillation signal is effectively but is not successful to separate the P wave. P waves drawing from each before 2/5 periods of heart are carried on the analysis. We get a method to measure the P wave time that it can help the doctors.
引文
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[8].温凌峰,孟兆辉,张永红,白净.QRS波群检测方法的新进展.国外医学生物医学分册,2001:24(5):193-197.
[9].Pan J and Tompkins WJ.A Real time QRS Detection Algorithm[J].IEEE Trans BME.1985.32:230-236.
[10].宁新宝,李德华,丁宋威.12导同步心电图波形检测与识别的研究[J].南京大学学报(自然科学).2004,1(40):129-136.
[11].沈虹,张良震,秦玮.用于识别心电图的BP网络系统[J].上海生物医学工程:1998,19(4):14-18.
[12].王继成,吕维雪.基于神经网络的一种心电图分类法[J].中国生物医学工程学报.1995,14(3):211-217.
[13].王继成.一种基干模糊神经网络的模式分类系统[J].计算机研究与发展:1999,36(1):26-30.
[14].Guian dinh duong.Parrern Recongnition of Abnormal ECG Signal Using Back-propagation Neural Network[J].A thesis of Master degree in the university of Oklahoma:1991.7-17.
[15].Cuiweili.Detection of ECG characteristic points using wavelet transforms[J].IEEE Trans on BME,1995.42(1):21-28.
[16].J.S.Sahambi.Using wavelet transforms for ECG characterization[J].IEEE Eng.Med.Biol,1996.16(1):77-83.
[17].余辉,张凯,吕扬生,等.二次微分小波在心电图QRS波检测中运用[J].中国医疗器械杂志,2001,25(6):334-337.
[18].张凯,顾长华,吕扬生,等.心电信号自动检测的小波变换方法[J].扬州大学学报(自然科学版)2001,20(2):199-225.
[19].陈作炳,钱湘萍,李春光.用小波变换对心电图进行分析[J].武汉理工大学学报,2001,23(5):27-29.
[20].姬军,董秀珍,王海滨,等.心电信号QRS波的识别算法及程序设计[J]一匕京生物医学工程,2001,20(2):119-122.
[21].M.E.Nygarda,L.Sommo,Delineation of the QRS Complex Using the Envelope of the ECG[J],Med.& Bio.Eng.& Compute,1983,21(5):538-547.
[22].尹登峰.动态ECG分析中QRS波检测算法的研究:[硕士论文].浙江:浙江大学.2003.
[23].DS.Benites.PA Gaydeoki.A Zaidi and AP Fitzpatrick,a New QRS Detection Algorithm Based on the Hilbert Transform.IEEE Computers in cardiology.2000,27:397-382.
[24].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:[博士论文].重庆:重庆大学.2003:61-63.
[25].Haykin.Neural network s-a comprehensive foundation [M].NewYork:Macmillan College Publishing Company,1994.
[26].O ja E.A simplified neuron model as a p principal component analyzer[J].J Math Biology,1982,15:267-273.
[27].O ja E,Karhunen J.On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix[J].J Math Anal Appli,1985,106:69-84.
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