基于EMD的舒張期心雜音信號的分析與識別研究
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本文關(guān)鍵詞：基于EMD的舒張期心雜音信號的分析與識別研究 出處：《重慶大學》2016年碩士論文 論文類型：學位論文
更多相關(guān)文章： 舒張期心雜音 端點效應(yīng) 經(jīng)驗?zāi)Ｊ椒纸?/b> Mel頻率倒譜系數(shù) 隱馬爾科夫模型
【摘要】：隨著經(jīng)濟的快速發(fā)展,人類飲食結(jié)構(gòu)的不斷改變,心血管疾病的發(fā)病率和死亡率均在迅速上升,嚴重威脅著人類的健康和幸福生活。心音是由心臟機械運動產(chǎn)生的振動信號,蘊含著與心血管疾病有關(guān)的大量診斷信息,因此,心音分析對于無創(chuàng)診斷心血管疾病具有重要的價值。心音的分類識別作為心音分析領(lǐng)域中的一個研究熱點,其目的是利用分類器根據(jù)從不同心音中提取的特征參數(shù)判定所屬疾病類型,而目前心音的特征提取與分類方法大多數(shù)是基于心音信號線性時變或時不變模型,而心雜音作為一種非線性非平穩(wěn)的信號,線性的分析方法勢必會忽視信號內(nèi)部的一些重要信息。因此,本文提出基于經(jīng)驗?zāi)Ｊ椒纸?Empirical Mode Decomposition,EMD)的舒張期心雜音信號的特征提取與分類方法。首先,在分析心音和心雜音產(chǎn)生的生理機制和臨床意義的基礎(chǔ)上,篩選舒張期心雜音信號作為實驗對象,從而有效的避免生理性雜音的干擾。針對適用于分析非線性、非平穩(wěn)性信號的EMD在分解過程中產(chǎn)生的端點效應(yīng),提出比例延拓結(jié)合鏡像延拓的方法給予抑制,數(shù)值信號和心音信號的測試表明該方法可以有效的減輕端點效應(yīng)對EMD分解的影響。其次,在心音去噪方面,利用小波變換方法來實現(xiàn),小波的三個重要參數(shù)小波基函數(shù)、分解層數(shù)和閾值通過3組實驗來確定;心音定位方面,在由希爾伯特變換獲取信號包絡(luò)的基礎(chǔ)上采用雙閾值的方法對心音準確定位。在心音特征值提取方面,提出了基于EMD的特征提取方法:在EMD分解獲取固有模態(tài)函數(shù)(Intrinsic Mode Function,IMF)的基礎(chǔ)上,采用互相關(guān)系數(shù)準則篩選出主IMF分量(IMF1~IMF4),分別提取其Mel頻率倒譜系數(shù)(Mel Frequency Cepstrum Coefficient,MFCC)、MFCC的一階差分系數(shù)(△MFCC)及Delta特征,將其組合形成3個特征向量MFCC、MFCC+△MFCC及MFCC+Delta,簡記為E+MFCC、E+M+△MFCC及E+M+Delta;針對EMD分解過程中的模態(tài)混疊問題而影響特征參數(shù)提取的準確性,采用總體平均經(jīng)驗?zāi)Ｊ椒纸?Ensemble Empirical Mode Decomposition,EEMD)代替EMD的方法來對心音信號進行分解獲取IMF分量,對篩選出來的主IMF分別提取MFCC、△MFCC、Delta特征,經(jīng)組合形成三個特征向量MFCC、MFCC+△MFCC及MFCC+Delta,簡記為EE+MFCC、EE+M+△MFCC及EE+M+Delta。最后,選擇具有通過較少樣本就能訓練出較為可靠模型的隱馬爾科夫模型(hidden Markov model,HMM)作為分類器,選取臨床采集到的正常心音和兩類舒張期心雜音(也即主動脈關(guān)閉不全和二尖瓣狹窄)作為實驗對象,訓練樣本與測試樣本的比例為1:2,利用所提取到的特征向量來建立模型進行心音的分類識別。最終實驗結(jié)果表明,所提出的2種特征參數(shù)提取方法的識別性能均優(yōu)于傳統(tǒng)的MFCC。同時為了進一步驗證所提出的端點延拓方法的有效性,將其用于EMD分解提取特征參數(shù),實驗結(jié)果表明,該延拓方法所獲得的識別率要高于未經(jīng)端點處理EMD方法的識別率。
[Abstract]:With the rapid development of economy and the change of human diet, the morbidity and mortality of cardiovascular disease are increasing rapidly. Heart sound is a vibration signal produced by mechanical movement of the heart, which contains a lot of diagnostic information related to cardiovascular disease. The analysis of heart sounds is of great value in the non-invasive diagnosis of cardiovascular diseases. The classification and recognition of heart sounds is a hot topic in the field of heart sound analysis. The purpose of this method is to use the classifier to determine the disease type according to the characteristic parameters extracted from different heart sounds. At present, most of the feature extraction and classification methods of heart sounds are based on linear time-varying or time-invariant models of heart sounds. As a kind of nonlinear and non-stationary signal, the linear analysis method will inevitably ignore some important information inside the signal. This paper presents a method for feature extraction and classification of diastolic cardiac murmur signals based on empirical Mode decomposition (EMD). On the basis of analyzing the physiological mechanism and clinical significance of cardiac murmur and cardiac murmur, the diastolic cardiac murmur signal was selected as the experimental object, so as to avoid the interference of physiological murmur effectively. The endpoint effect of the EMD of non-stationary signal in the decomposition process is suppressed by the method of proportional continuation combined with mirror continuation. The test of numerical signal and heart sound signal shows that this method can effectively reduce the effect of endpoint effect on EMD decomposition. Secondly, in the aspect of heart sound denoising, wavelet transform is used to realize it. Wavelet basis function, decomposition layer number and threshold value of three important parameters of wavelet are determined by three groups of experiments. In the aspect of heart sound localization, based on the Hilbert transform to obtain the signal envelope, the method of double threshold is used to locate the heart sound accurately, and the characteristic value of heart sound is extracted. A method of feature extraction based on EMD is proposed: the intrinsic Mode function is obtained by EMD decomposition. The main IMF component (IMF1 / IMF4) was screened by using the correlation number criterion. The Mel cepstrum coefficients of Mel Frequency Cepstrum coefficients were extracted respectively. The first order difference coefficient (MFCC) and Delta characteristics of MFCC are combined into three characteristic vectors: MFCC MFCC and MFCC Delta. The results were summarized as E MFCC E M MFCC and E M DeltaA. The accuracy of feature parameter extraction is affected by the modal aliasing problem in the process of EMD decomposition. Ensemble Empirical Mode Decomposition was decomposed by the overall average empirical mode. EEMD) replaces the EMD method to decompose the heart sound signal to obtain the IMF component, and extracts the MFCC, MFCC / Delta features from the selected master IMF. Three characteristic vectors, MFCC MFCC and MFCC Delta. are formed by combination, which can be abbreviated as EE MFCC. EE M MFCC and EE M Delta. finally. The hidden Markov model (HMMM), which can train a more reliable model with fewer samples, is selected as the classifier. The normal heart sounds and two kinds of diastolic murmur (that is aortic insufficiency and mitral stenosis) were selected as experimental subjects. The ratio of training sample to test sample was 1: 2. The extracted eigenvector is used to establish the model for the classification and recognition of heart sounds. Finally, the experimental results show that. In order to further verify the effectiveness of the proposed endpoint continuation method, the proposed method is used to extract feature parameters by EMD decomposition. The experimental results show that the recognition rate of the extension method is higher than that of the untreated EMD method.
【學位授予單位】：重慶大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：R540.4;TN911.6
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