本文選題：心音　切入點：盲分離　出處：《華南理工大學》2012年碩士論文　論文類型：學位論文

【摘要】：心血管疾病是嚴重威脅人類健康的循環(huán)系統(tǒng)的疾病，心音信號是一種可以反映早期心臟病變的重要生理信號，尤其對瓣膜疾病的臨床前診斷有著重要作用，對其的研究和分析受到越來越多學者的關(guān)注。 臨床上使用聽診器拾取心音，，通過醫(yī)生的聽覺系統(tǒng)被感知，依靠醫(yī)生的經(jīng)驗做出診斷，容易受到主觀因素的影響。在科研中常使用傳感器攝取，借助放大電路等對心音信號進行調(diào)理，形成振動波形式的心音圖，客觀可見，便于保存，有助于進行深入分析心臟的生理狀態(tài)。 心臟正常的收縮舒張產(chǎn)生正常心音，對其進行研究，可以獲得心率、明確心動周期，有利于對心臟的正常生理機能做出判斷。心臟器質(zhì)性病變等會產(chǎn)生心雜音。心雜音情況復(fù)雜，頻率多變，振幅不一。傳統(tǒng)的心音聽診難以準確區(qū)分心雜音和正常心音，心雜音混合在正常心音中，既干擾了對正常心音的分析，又不利于判斷心臟病變。本文設(shè)計了一種基于LabView的時序同步雙傳感器系統(tǒng)，一傳感器放置于心雜音的主要聽診區(qū)，獲取包含心雜音成分的信息，另外一個傳感器置于遠離心雜音傳導(dǎo)通路的心臟聽診區(qū)以降低雜音對正常心音的干擾，同步采集正常心音和心雜音權(quán)重不同的兩路信號，作為兩道參考信號輸入盲分離算法網(wǎng)絡(luò)，利用盲分析算法分離正常心音和心雜音，在此基礎(chǔ)上對二者特征做單獨分析。 在源信號未知的情況下，盲分離技術(shù)可以解決從觀測到的混迭信號中分離出源信號的問題，是信號處理領(lǐng)域研究的熱點內(nèi)容。本文首次將其應(yīng)用于心音和心雜音的分離處理中。將正常心音形成系統(tǒng)作為一個振動源，而病理性心雜音產(chǎn)生的振動作為單獨聲源，在體表得到的心音圖可以看做是兩個源信號經(jīng)心胸傳導(dǎo)系統(tǒng)混疊之后的信號。以中心極限理論為主要依據(jù)，以峭度作為分離結(jié)果非高斯性質(zhì)的衡量，采用梯度算法尋求最優(yōu)解，對多例瓣膜病變信號進行了盲分離處理，重新構(gòu)建心音的正常部分和雜音部分，并重點分析了二尖瓣狹窄和關(guān)閉不全兩種病變導(dǎo)致的不同雜音，得到的時頻特性可以很好的解釋產(chǎn)生病變的原因，為實現(xiàn)瓣膜疾病的數(shù)字化診斷提供依據(jù)。
[Abstract]:Cardiovascular disease is a serious threat to human health of the circulatory system disease, the heart sound signal is a kind of early heart disease can reflect the important physiological signal, especially for valvular disease preclinical diagnosis has an important role. More and more scholars pay attention to its research and analysis. Clinical use of stethoscope to pick up heart sounds, sense through the doctor's auditory system, rely on the doctor's experience to make a diagnosis, easy to be affected by subjective factors. With the help of amplifying circuit, the heart sound signal can be adjusted to form the heart sound picture in the form of vibration wave, which can be seen objectively and is easy to preserve, which is helpful for the further analysis of the physiological state of the heart. The normal systolic and diastolic activity of the heart produces normal heart sounds, which can be studied to obtain the heart rate and determine the cardiac cycle. It is helpful to judge the normal physiological function of the heart. Heart diseases, such as organic diseases, produce cardiac murmur. The murmur of the heart is complex, the frequency is variable, and the amplitude is different. Traditional heart-sound auscultation is difficult to distinguish the murmur from the normal cardiac murmur accurately. The mixing of cardiac murmurs in normal heart sounds not only interferes with the analysis of normal heart sounds, but also does not help to judge heart disease. In this paper, a timing synchronous dual sensor system based on LabView is designed, one sensor is placed in the main auscultation area of heart murmur. The other sensor is placed in the heart-auscultation area away from the cardiac murmur conduction pathway to reduce the interference of the murmur to the normal heart sound, and simultaneously collect the two signals with different weights of normal heart sounds and cardiac murmurs. As the input of two reference signals, the blind analysis algorithm is used to separate normal heart sounds and heart murmur. When the source signal is unknown, the blind separation technique can solve the problem of separating the source signal from the observed mixed signal. It is a hot topic in the field of signal processing. In this paper, it is first applied to the separation of heart sounds and cardiac murmurs. The normal heart sound forming system is regarded as a vibration source, while the pathological heart murmur is used as a single sound source. The cardiogram obtained on the body surface can be regarded as the signal of two source signals mixed by the cardiothoracic conduction system. Based on the central limit theory and the kurtosis as the measure of the non-#china_person0# property of the separation result, the gradient algorithm is used to find the optimal solution. In this paper, the signals of several valvular lesions were separated, and the normal and murmur parts of the heart sound were reconstructed, and the different murmur caused by mitral stenosis and insufficiency were analyzed. The obtained time-frequency characteristics can explain the causes of the disease and provide the basis for digital diagnosis of valvular diseases.
【學位授予單位】：華南理工大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：R318.0

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