心音信号分析和识别系统的开发
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摘要
心音信号是人体最重要的生理信号之一,心音检测是了解心脏状态的一项重要手段,具有心电检测不可取代的临床价值。人工心脏瓣膜钙化后瓣膜关闭音的主频有向高频方向变化的趋势,因此可以利用瓣膜关闭音的频谱来探测人工心脏瓣膜的退化程度;第一心音的幅度是心肌收缩能力的标准度量,因此可以利用心音对心肌收缩能力进行评估;心音图检查将心脏听诊形象化,提高了心脏疾病的诊断和鉴别诊断水平。对心音信号进行分析并提取其特征参数,便于医生了解心脏的基本情况及做出更加准确的判断。
心音信号是非平稳信号,为了全面了解心音信号的特征,需要从时域、频域、时-频域三个角度对心音信号进行分析,提取特征参数。由于各种干扰的存在,采集到的心音信号质量较差,因此在进行分析之前要对心音信号进行预处理,消除其包含的各种噪声。经过预处理后,心音信号质量显著提升,可用于进一步分析。
本文基于希尔伯特变换、短时傅里叶变换、小波分析和黄变换等理论方法,结合临床实践的需求,对心音分析方法进行了较为深入的研究,并在此基础上设计开发了心音分析系统。论文的主要工作如下:
(1)为了消除心音信号中的低频噪声,本文提出了基于黄变换的滤波方法,它通过经验模式分解将信号分解成一系列的固有模态函数,根据固有模态函数频率的不同可以将低频噪声分离出来。与小波滤波相比,该方法能有效地消除心音信号中的低频噪声。
(2)为了得到更准确的时域特征,本文利用希尔伯特变换进行包络提取。希尔伯特变换可以将信号转化为解析信号,对解析信号求模即可得到信号的包络。与归一化平均香农能量相比,该方法不仅结果更加准确,在出现心杂音时也能正确提取心音信号的包络。
(3)为了将时-频分析的结果定量化,本文对传统的时-频分析方法进行了改进。由于时-频分析的结果是三维数据,很难定量提取心音信号的特征,为了得到心音信号的时间-频率二维分布图,本文在时域内对时-频分析的结果进行分段,每段内取幅值最大点对应的频率作为该段的频率值,据此可以得到心音在某一时刻的频率情况。
(4)为了使心音分析系统便于操作,本文采用Matlab GUI作为开发平台开发了心音分析系统。Matlab GUI是Matlab提供的一个可视化图形界面开发环境,它利用各种控件完成界面设计,使用回调函数调用程序实现各种功能。
论文最后对全文进行了总结,并对下一步的研究工作进行了展望。
Heart sound signal is one of the most primary physiological signals. Detection of heart sounds is an important method with judging the state of heart which has its own advantages that ECG can not replace. Close of heart valves has a voice, the value of whose main frequency will increase if artificial heart valves is calcified. So degradation assessment of artificial heart valves can be detected with the spectrum of close voice. Amplitude of the first heart sound is the normal measurement of cardiac contractility, therefore, the heart sound can be used to evaluate cardiac contractility. Phonocardiograms (PCGs) visualize cardiac auscultation, and improve the diagnosis level of cardiovascular diseases. It is convenient to analyze the heart sound and extract the characteristic parameter for doctors to obtain basic information of heart and make more exact diagnosis.
The PCG signal is non-stationary. In order to get a comprehensive understanding of the characteristic for the heart sound, it is necessary to analyze heart sound in time, frequency and time-frequency domain. Due to the interference, the heart sound signal is noised so that the preprocessing is necessary to remove the noises. After the preprocessing, the quality of signal is improved and can be used for further analysis.
Based on the theories of Hilbert Transform, Short Time Fourier Transform (STFT), Wavelet analysis and Huang Transform, this paper makes a deeper research on the analysis of heart sound and the design for heart sound analysis system. The main contents are listed below:
(1) To remove the low frequency noise in heart sound, a new method based on Huang Transform is proposed in this paper. It may decompose signal into series of intrinsic mode function (IMF) with empirical mode decomposition (EMD) and the low frequency noise can be isolated because of its different of frequency in this process. The experiment results show that this method can remove the low frequency noise more effectively than the wavelet filter.
(2)To make characteristic in time domain more accurate, Hilbert Transform is used to extract the envelope of heart sound. The signal is transformed to the analytical signal by Hilbert Transform and the absolute value of that is the envelope of heart sound. Compared with normalized average shannon energy, this method not only can get more accurate result but also can extract envelope when heart murmurs occur.
(3) In order to get quantitative analysis for the result of time-frequency, the traditional methods on time-frequency analysis are improved. Because the result is three dimensional data, it is difficult to get characteristic quantitatively. In order to get two dimensional distribution on time-frequency, the result is divided into several parts in time domain. In every part, frequency which has the biggest amplitude is the adaptive frequency for this part. With this method, the frequency of every time can be obtained.
(4) To operate heart sound analysis system easily, the system is developed by Matlab GUI which is a graphical interface development environment provided by Matlab. The interface is designed with control modules and the function is implemented with callback functions.
Finally, we make a conclusion and propose the future research directons in this field.
心音信号是非平稳信号,为了全面了解心音信号的特征,需要从时域、频域、时-频域三个角度对心音信号进行分析,提取特征参数。由于各种干扰的存在,采集到的心音信号质量较差,因此在进行分析之前要对心音信号进行预处理,消除其包含的各种噪声。经过预处理后,心音信号质量显著提升,可用于进一步分析。
本文基于希尔伯特变换、短时傅里叶变换、小波分析和黄变换等理论方法,结合临床实践的需求,对心音分析方法进行了较为深入的研究,并在此基础上设计开发了心音分析系统。论文的主要工作如下:
(1)为了消除心音信号中的低频噪声,本文提出了基于黄变换的滤波方法,它通过经验模式分解将信号分解成一系列的固有模态函数,根据固有模态函数频率的不同可以将低频噪声分离出来。与小波滤波相比,该方法能有效地消除心音信号中的低频噪声。
(2)为了得到更准确的时域特征,本文利用希尔伯特变换进行包络提取。希尔伯特变换可以将信号转化为解析信号,对解析信号求模即可得到信号的包络。与归一化平均香农能量相比,该方法不仅结果更加准确,在出现心杂音时也能正确提取心音信号的包络。
(3)为了将时-频分析的结果定量化,本文对传统的时-频分析方法进行了改进。由于时-频分析的结果是三维数据,很难定量提取心音信号的特征,为了得到心音信号的时间-频率二维分布图,本文在时域内对时-频分析的结果进行分段,每段内取幅值最大点对应的频率作为该段的频率值,据此可以得到心音在某一时刻的频率情况。
(4)为了使心音分析系统便于操作,本文采用Matlab GUI作为开发平台开发了心音分析系统。Matlab GUI是Matlab提供的一个可视化图形界面开发环境,它利用各种控件完成界面设计,使用回调函数调用程序实现各种功能。
论文最后对全文进行了总结,并对下一步的研究工作进行了展望。
Heart sound signal is one of the most primary physiological signals. Detection of heart sounds is an important method with judging the state of heart which has its own advantages that ECG can not replace. Close of heart valves has a voice, the value of whose main frequency will increase if artificial heart valves is calcified. So degradation assessment of artificial heart valves can be detected with the spectrum of close voice. Amplitude of the first heart sound is the normal measurement of cardiac contractility, therefore, the heart sound can be used to evaluate cardiac contractility. Phonocardiograms (PCGs) visualize cardiac auscultation, and improve the diagnosis level of cardiovascular diseases. It is convenient to analyze the heart sound and extract the characteristic parameter for doctors to obtain basic information of heart and make more exact diagnosis.
The PCG signal is non-stationary. In order to get a comprehensive understanding of the characteristic for the heart sound, it is necessary to analyze heart sound in time, frequency and time-frequency domain. Due to the interference, the heart sound signal is noised so that the preprocessing is necessary to remove the noises. After the preprocessing, the quality of signal is improved and can be used for further analysis.
Based on the theories of Hilbert Transform, Short Time Fourier Transform (STFT), Wavelet analysis and Huang Transform, this paper makes a deeper research on the analysis of heart sound and the design for heart sound analysis system. The main contents are listed below:
(1) To remove the low frequency noise in heart sound, a new method based on Huang Transform is proposed in this paper. It may decompose signal into series of intrinsic mode function (IMF) with empirical mode decomposition (EMD) and the low frequency noise can be isolated because of its different of frequency in this process. The experiment results show that this method can remove the low frequency noise more effectively than the wavelet filter.
(2)To make characteristic in time domain more accurate, Hilbert Transform is used to extract the envelope of heart sound. The signal is transformed to the analytical signal by Hilbert Transform and the absolute value of that is the envelope of heart sound. Compared with normalized average shannon energy, this method not only can get more accurate result but also can extract envelope when heart murmurs occur.
(3) In order to get quantitative analysis for the result of time-frequency, the traditional methods on time-frequency analysis are improved. Because the result is three dimensional data, it is difficult to get characteristic quantitatively. In order to get two dimensional distribution on time-frequency, the result is divided into several parts in time domain. In every part, frequency which has the biggest amplitude is the adaptive frequency for this part. With this method, the frequency of every time can be obtained.
(4) To operate heart sound analysis system easily, the system is developed by Matlab GUI which is a graphical interface development environment provided by Matlab. The interface is designed with control modules and the function is implemented with callback functions.
Finally, we make a conclusion and propose the future research directons in this field.
引文
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[2]刘文秀.心脏听诊[M].北京:人民军医出版社,2005.
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[4]Stein P D,Sabbah H N,Lakier J B,et al.Frequency spectra of the first heart sound and of the aortic component of the second heart sound in patients with degenerated porcine bioprosthetic valves[J].American Journal of Cardiology,1984,53(4):557-561.
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[10]赵治栋,赵知劲,张嵩等.心音自动分段算法研究[J].航天医学与医学工程,2004,17(6):455-456.
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[2]刘文秀.心脏听诊[M].北京:人民军医出版社,2005.
[3]胡旭东,于秀章.心脏听诊与心音图[M].北京:人民卫生出版社,1981.
[4]Stein P D,Sabbah H N,Lakier J B,et al.Frequency spectra of the first heart sound and of the aortic component of the second heart sound in patients with degenerated porcine bioprosthetic valves[J].American Journal of Cardiology,1984,53(4):557-561.
[5]Durand L G,Blanchard M,Sabbah H N,et al.A Bayes model for automatic detection and quantification of bioprosthetic valve degeneration[J].Mathematical and Computer Modeling,1988,11:158-163.
[6]Durand L G,Langlois Y E,Lanthier T,et al.Spectral analysis and acoustic transmission of mitral and aortic valve closure sounds in dogs,Part1:Modeling the heart thorax acoustic system[J].Medical & Biological Engineering & Computing,1990,28(4):269-277.
[7]Rice M L,Doyle D J.Comparison of phonocardiographic monitoring locations[A].In:IEEE 17th Annual Conference[C],1995:685-686.
[8]元秀华,谢定,吴承德.心音信号测量中的噪音干扰分析与滤除方法[J].中国现代医学杂志,1999,9(6):65-67.
[9]周静,杨永明,何为.心音信号的分析及其特征提取方法的研究[J].中国生物医学工程学报,2005,24(6):685-689.
[10]赵治栋,赵知劲,张嵩等.心音自动分段算法研究[J].航天医学与医学工程,2004,17(6):455-456.
[11]元秀华,谢定,吴承德.心音信号测量中的噪音干扰分析与滤除方法[J].中国现代医学杂志,1999,9(6):65-66.
[12]姚晓帅,郭兴明,陈剑等.基于数学形态学的心音包络提取与识别方法研究[J]. 北京生物医学工程,2004,23(3):201-204.
[13]刘军,张祖兴,潘家华等.基于短时傅立叶变换和连续小波变换的先天性心脏病心音比较分析研究[J].北京生物医学工程,2006,28(S1):89-93.
[14]朱冰莲,吕佶.时频表达在心音研究中的应用[J].重庆大学学报,2006,29(5):116-119.
[15]周静,杨永明.心音信号的时频分析[J].重庆大学学报,2004,27(4):159-162.
[16]张建,沈民奋,宋骥.三种时频分析方法在心音信号分析中的应用[J].汕头大学学报,2003,18(2):62-69.
[17]全海燕,王维廉.基于小波多分辨率分析的第一第二心音提取[J].北京生物医学工程,2004,23(1):64-66.
[18]杜寿昌,梁虹.基于Hilbert-Huang变换的第一心音信号时频分析[J].云南民族大学学报,2004,13(2):95-98.
[19]杜寿昌,梁虹.心音的非线性时间序列分析[J].浙江大学学报,2006,40(8):1473-1476.
[20]李桥,邵庆余,朱兴雷等.心音数据库及彩色三维图谱库的建立[J].山东医科大学学报,2001,39(4):321-323.
[21]张小瑞,陈文福,李孟源等.基于LabVIEW的心音声发射信号分析仪[J].河南科技大学学报,2006,27(5):45-47.
[22]张孝桂,何为,周静等.基于嵌入式系统的便携式心音分析仪的研究[J].仪器仪表学报,2007,28(2):303-306.
[23]Durand L G,Deguise J,Cloutier G,et al.Evaluation of FFT-Based and modern parametric methods for the spectral analysis of bioprosthetic valve sounds[J].IEEE Transactions on Biomedical Engineering,1986,33(6):572-578.
[24]Liang H,Lukkarinen S,Hartimo I.Heart sound segmentation algorithm based on heart sound envelogram[A].In:Computers in Cardiology[C],1997:105-108.
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