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  本文選題：心雜音　切入點：魏格納變換　出處：《重慶大學(xué)》2012年碩士論文

【摘要】：心音信號是人體重要的生理信號之一，心音聽診不僅簡單無創(chuàng)且能較早的發(fā)現(xiàn)異常。由于心雜音信號的復(fù)雜性和非平穩(wěn)性，，采用現(xiàn)代數(shù)字信號處理方法對心雜音信號進行分析和識別成為了解心血管狀態(tài)不可或缺的手段。本文以臨床輔助診斷需求為目的對心雜音信號進行分析研究，內(nèi)容涉及心雜音形成機制分析、心雜音信號特征向量的提取、正常心音和4類病理性雜音信號（主動脈瓣狹窄、主動脈瓣反流、二尖瓣反流、肺動脈瓣狹窄）分類識別研究。 本課題的研究工作主要包括以下幾個方面： 1）正常心音和雜音信號的特征分析，介紹了正常心音和雜音形成的生理機制和時域波形特點，并對心音和雜音信號進行AR模型功率譜估計，為特征向量的選取提供一定的理論依據(jù)和參考。 2）心雜音信號多成分分離算法的實現(xiàn)。采用基于主成分分析的奇異譜方法，對心雜音信號進行奇異值分解和重構(gòu)，達到正常心音成分和雜音成分分離的效果，結(jié)果表明該方法能有效的抑制信號魏格納變換產(chǎn)生的交叉項干擾。 3）心雜音信號時頻能量譜方法研究。文中對比分析了常見的幾種信號時頻方法：快速傅里葉變換，小波變換和魏格納變換�？焖俑道锶~變換得到的時頻能量譜圖，分辨率受窗函數(shù)寬度影響很大，小波變換可以得到尺度-能量譜，但基小波和尺度的選取不易。因此，本文最后采用魏格納變換對正常心音和雜音信號進行時頻分析，得到的二維時頻能量譜分辨率高，能較好的反映正常心音和雜音信號時頻域和能量方面的特征。 4）心雜音信號特征向量提取。對3M Littmann Stethoscopes數(shù)據(jù)庫中正常心音和四種類型的雜音信號進行魏格納變換得到聯(lián)合時頻能量譜，從中分析時域，頻域和能量三方面的特征值，比如雜音持續(xù)時間，雜音峰值頻率，雜音能量分數(shù)等。以此作為特征向量，為心音和雜音的分類識別提供依據(jù)。 5）心雜音信號分類分析方法。由于病理心雜音樣本數(shù)量有限，因此選擇支持向量機。課題研究了支持向量機核函數(shù)，多分類支持向量機的選取方法，選用以分類精度最大為判斷準則網(wǎng)格優(yōu)化方法來確定核函數(shù)參數(shù)和松弛變量最優(yōu)值的選取，建立了適合心雜音分類的支持向量機模型。實驗中選取正常心音和四類常見的病理心雜音樣本：主動脈瓣狹窄，主動脈瓣反流，二尖瓣反流和肺動脈瓣狹窄心雜音每種類型40例進行測試，按照訓(xùn)練集樣本數(shù)和測試集樣本數(shù)之比為3:1的模式進行學(xué)習(xí)和測試。實驗結(jié)果表明分類精度較高，均達到了90%以上，驗證了算法的有效性。
[Abstract]:Heart sound signal is one of the important physiological signals in human body. Heart-sound auscultation is not only simple and noninvasive, but also can detect abnormality earlier.Because of the complexity and nonstationarity of the cardiac murmur signal, it is an indispensable means to analyze and recognize the cardiac murmur signal by using the modern digital signal processing method.The purpose of this paper is to analyze and study cardiac murmur signals for the purpose of clinical assistant diagnosis, including the analysis of the mechanism of cardiac murmur formation, the extraction of characteristic vectors of cardiac murmur signals, normal heart sounds and 4 kinds of pathological murmur signals (aortic stenosis).Classification and recognition of aortic regurgitation, mitral regurgitation and pulmonary valve stenosis.The research work of this topic mainly includes the following aspects:1) analyzing the characteristics of normal heart sounds and murmur signals, introducing the physiological mechanism and time domain waveform characteristics of normal heart sounds and murmur signals, and estimating the AR model power spectrum of heart sounds and murmur signals.It provides some theoretical basis and reference for the selection of feature vectors.2) the realization of multi-component separation algorithm for cardiac murmur signal.The singular spectrum method based on principal component analysis (PCA) is used to decompose and reconstruct the singularity value of the heart murmur signal, so as to achieve the effect of separating the normal heart sound component from the murmur component.The results show that this method can effectively suppress the crossover interference generated by signal Wigner transform.3) study on time-frequency energy spectrum of cardiac murmur signal.In this paper, several signal time-frequency methods are compared and analyzed: fast Fourier transform (FFT), wavelet transform (WT) and Wigner transform (Wigner transform).The resolution of the time-frequency energy spectrum obtained by the fast Fourier transform is greatly influenced by the width of the window function. The wavelet transform can obtain the scale-energy spectrum, but it is difficult to select the basis wavelet and the scale.Therefore, in the end, we use Wigner transform to analyze the signal of normal heart sound and murmur. The result shows that the two dimensional time-frequency energy spectrum has high resolution and can reflect the characteristics of normal heart sound and murmur signal in time-frequency domain and energy domain.4) feature vector extraction of cardiac murmur signal.Using Wigner transform to obtain the joint time-frequency energy spectrum of normal heart sounds and four types of murmur signals in the 3M Littmann Stethoscopes database, the time domain, frequency domain and energy characteristic values are analyzed, such as the duration of the murmur, the peak frequency of the murmur.Noise energy fraction, etc.It is used as the feature vector to provide the basis for the classification and recognition of heart sounds and murmurs.5) Classification and analysis method of cardiac murmur signal.Because of the limited number of pathological heart murmur samples, support vector machine was chosen.In this paper, the kernel function of support vector machine and the selection method of multi-classification support vector machine are studied. The optimal value of kernel function parameter and relaxation variable is determined by mesh optimization method with the maximum classification accuracy as the criterion.A support vector machine model suitable for classification of heart murmur is established.Normal heart sounds and four common pathological heart murmur samples were tested in 40 patients with aortic stenosis, aortic regurgitation, mitral regurgitation and pulmonary stenosis.Study and test according to the ratio of training set sample number to test set sample number is 3:1.The experimental results show that the classification accuracy is higher than 90%, and the validity of the algorithm is verified.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2012
【分類號】：R318.0;TN911.7

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