本文關(guān)鍵詞： 心音 LabVIEW 預(yù)處理模塊 特征提取模塊 模型訓(xùn)練與識(shí)別模塊　出處：《重慶大學(xué)》2014年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：心血管疾病已給人類(lèi)帶來(lái)了新的嚴(yán)峻挑戰(zhàn)，并已經(jīng)成為全球性公共衛(wèi)生問(wèn)題。心音是心臟和心血管系統(tǒng)機(jī)械運(yùn)動(dòng)狀況的反映，因此，心音檢測(cè)逐步成為臨床輔助診斷心血管疾病的有效方法之一。對(duì)心音信號(hào)的臨床檢測(cè)常用方法是心音聽(tīng)診和心音圖，但是心音聽(tīng)診技術(shù)容易受到人耳聽(tīng)力靈敏度和臨床醫(yī)師主觀經(jīng)驗(yàn)的影響，心音圖能夠彌補(bǔ)心音聽(tīng)診的一些不足，但是它也存在著一些缺點(diǎn)，在一定程度上限制了它的應(yīng)用。近年來(lái)，隨著計(jì)算機(jī)和信號(hào)處理應(yīng)用的逐步推廣，心音分析儀的設(shè)計(jì)與開(kāi)發(fā)已成為心音分析領(lǐng)域的發(fā)展趨勢(shì)。目前出現(xiàn)的心音分析儀多數(shù)雖然可以對(duì)心音進(jìn)行簡(jiǎn)單的時(shí)頻分析，但是基本上都不具有對(duì)心音的分類(lèi)識(shí)別功能，因此，心音分析儀的功能有待于進(jìn)一步完善。鑒于此，本文在LabVIEW平臺(tái)上設(shè)計(jì)了一款心音分類(lèi)識(shí)別系統(tǒng)。 本文首先給出了系統(tǒng)設(shè)計(jì)的整體方案，該系統(tǒng)一共分為三個(gè)子系統(tǒng)：預(yù)處理模塊、特征提取模塊、模型訓(xùn)練與識(shí)別模塊。預(yù)處理模塊，又分為以下四個(gè)模塊：去噪模塊、預(yù)加重模塊、分幀加窗模塊和端點(diǎn)檢測(cè)模塊。其中，，去噪模塊是利用小波變換去噪算法來(lái)設(shè)計(jì)的，通過(guò)實(shí)驗(yàn)確定了小波母函數(shù)、分解尺度和閾值的合適選�。活A(yù)加重模塊是通過(guò)一個(gè)一階數(shù)字濾波器來(lái)實(shí)現(xiàn)的；利用哈明窗設(shè)計(jì)了分幀加窗模塊；端點(diǎn)檢測(cè)模塊是基于短時(shí)能量和短時(shí)過(guò)零率的原理來(lái)實(shí)現(xiàn)的。 其次，在特征參數(shù)提取模塊，本文介紹Mel頻率倒譜系數(shù)及對(duì)其改進(jìn)的其它四個(gè)參數(shù)：Mel頻率倒譜系數(shù)結(jié)合其一階差分系數(shù)、Mel頻率倒譜系數(shù)結(jié)合其Delta特征、小波包變換改進(jìn)的Mel頻率倒譜系數(shù)、小波包變換改進(jìn)的Mel頻率倒譜系數(shù)結(jié)合其一階差分系數(shù)(DWPTMFCC+ΔDWPTMFCC)的提取原理，同時(shí)詳細(xì)分析了它們?cè)贚abVIEW平臺(tái)上提取的難點(diǎn)并給出了解決方案。 最后，在模型訓(xùn)練與識(shí)別模塊，本文選取常見(jiàn)的識(shí)別模型——高斯混合模型(GMM)用于心音信號(hào)的分類(lèi)識(shí)別。介紹了GMM的原理，分析了傳統(tǒng)GMM的參數(shù)初始化算法K-means算法存在的缺點(diǎn)和不足，針對(duì)此，提出了三種算法：近似模糊C均值聚類(lèi)算法、加權(quán)模糊C均值聚類(lèi)算法和加權(quán)可選擇模糊C均值聚類(lèi)算法(WOFCM)對(duì)傳統(tǒng)的GMM加以改進(jìn)。此模塊的關(guān)鍵點(diǎn)是這四個(gè)識(shí)別模型的訓(xùn)練過(guò)程與識(shí)別過(guò)程在LabVIEW平臺(tái)上的實(shí)現(xiàn)與設(shè)計(jì)。 將以上各個(gè)子模塊集成在一起，就可以完成最終整體系統(tǒng)的設(shè)計(jì)。從特征參數(shù)和識(shí)別模型這兩個(gè)角度對(duì)設(shè)計(jì)的系統(tǒng)進(jìn)行測(cè)試，即對(duì)本文選取的臨床上采集的正常心音信號(hào)和二尖瓣狹窄、主動(dòng)脈瓣狹窄、主動(dòng)脈瓣關(guān)閉不全、室間隔缺損、肺動(dòng)脈瓣狹窄、心律不齊、二尖瓣關(guān)閉不全、S1分裂、S2分裂9類(lèi)病理信號(hào)進(jìn)行分類(lèi)識(shí)別，綜合考慮識(shí)別率和識(shí)別時(shí)間，當(dāng)DWPTMFCC+ΔDWPTMFCC作為特征參數(shù)和WOFCM改進(jìn)得到的GMM作為識(shí)別模型時(shí)表現(xiàn)出最為優(yōu)越的識(shí)別性能，尤其是對(duì)異常心音信號(hào)而言，提高程度更為顯著。因此，利用該參數(shù)和模型對(duì)最初設(shè)計(jì)的整個(gè)系統(tǒng)進(jìn)行了簡(jiǎn)化處理，得到了最終的心音分類(lèi)識(shí)別系統(tǒng)，這對(duì)目前的心音分析儀的功能進(jìn)行進(jìn)一步的補(bǔ)充完善，在研究心臟活動(dòng)和早期的臨床心血管疾病的診斷中有潛在的應(yīng)用價(jià)值。
[Abstract]:Cardiovascular disease has brought new challenges, and has become a global public health problem. The heart sound is reflected in the heart and cardiovascular system of mechanical motion so that the heart sound detection has gradually become one of the effective methods for clinical diagnosis of cardiovascular disease. The common clinical detection method of heart sound signal is auscultation and phonocardiogram, influence but auscultation technique is vulnerable to human hearing sensitivity and the clinician subjective experience, PCG can remedy some shortcomings of auscultation, but it also has some shortcomings, which limits its application to some extent. In recent years, with the popularization of computer and signal processing applications, design and development of heart sound analyzer has a trend in the field of heart sound analysis. Although the majority of the heart sound analysis can be carried out at the frequency of heart sound simple However, the function of heart sound analyzer needs to be further improved. In view of this, a heart sound classification and recognition system is designed on the LabVIEW platform.
This paper first gives the overall scheme of system design, the system is divided into three subsystems: pre-processing module, feature extraction module, model training and recognition module. The pretreatment module, is divided into the following four modules: denoising module, preprocessing module, window module and endpoint detection module. The denoising module is to design the denoising algorithm using wavelet transform, wavelet function was determined by the experiment, the appropriate selection of scale and threshold decomposition; preprocessing module is realized by a digital filter; design of window module using Hamming window; endpoint detection module is the principle of short time energy and short-time zero crossing rate based.
Secondly, in the feature extraction module parameters, this paper introduces the Mel frequency cepstrum coefficient and the improvement of the other four parameters: the first order differential coefficient with Mel frequency cepstral coefficients, Mel frequency cepstral coefficients with the Delta feature and Mel frequency cepstrum coefficients of wavelet packet transform is improved, the first-order differential coefficient with frequency of Mel cepstrum coefficients of wavelet packet transform improved (DWPTMFCC+ DWPTMFCC) extraction principle, we analyze the problems and give them the extraction on the LabVIEW platform solutions.
Finally, in the model training and recognition module, this paper selects the recognition of Gauss mixture model (GMM) model commonly used for classification and recognition of heart sound signals. This paper introduces the principle of GMM, analyzes the existing parameter initialization algorithm of traditional K-means algorithm GMM the shortcomings and deficiencies, in view of this, we proposed three kinds of algorithms: approximate fuzzy C means clustering algorithm, weighted fuzzy C means clustering algorithm and weighted fuzzy C mean clustering algorithm (WOFCM) for the GMM to be improved. The key point of this module is the design and realization process of training and recognition of the four identification model on the LabVIEW platform.
Integration of the above sub modules together, we can achieve the design of the whole system. The final design of the testing system from the two aspects of feature parameters and recognition model, namely to collect clinical selected on the normal heart sound and mitral stenosis, aortic stenosis, aortic insufficiency, ventricular septal defect, arrhythmia, pulmonary valve stenosis, mitral regurgitation, S1 division, S2 division of 9 kinds of pathological signal classification, considering the recognition rate and the time when the DWPTMFCC+ Delta DWPTMFCC as characteristic parameters and WOFCM improved GMM as the recognition model showing the most superior recognition performance, especially in terms of abnormal heart sound signal, improving the degree is more significant. Therefore, the model parameters and the initial design of the whole system is simplified, the final classification of heart sound recognition system It will further improve the function of the current heart sound analyzer, and has potential application value in the study of cardiac activity and early clinical cardiovascular disease.

【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN911.7

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