發(fā)布時(shí)間：2018-11-06 09:18

【摘要】：先天性心臟病已經(jīng)給人類的生活與健康帶來了很大的影響,并且其發(fā)病率一直居高不下,據(jù)不完全統(tǒng)計(jì),全球每年大約新增150萬先天性心臟病患者,并且有將近六成的患者得不到及時(shí)的治療而引起病情延誤甚至死亡,但是要是及時(shí)發(fā)現(xiàn)和診治,多數(shù)患者都能夠恢復(fù)正常。目前臨床上的各種診斷方法都存在一定的缺陷,而心音信號能夠表現(xiàn)出人體狀態(tài)變化和心臟病變的病理信息,所以可以使用數(shù)字信號處理方法對信號展開分析,給臨床診斷結(jié)果提供參考。 本文將以MATLAB為平臺從信號預(yù)處理、特征分析、提取與識別這三個(gè)方面來對心音信號開展分析研究。 預(yù)處理包含了降噪與分段定位。本文首先采用了小波閾值消噪算法消除噪聲,并且比較了這個(gè)算法中選擇不同的閾值和小波基對降噪效果的影響。然后采用不借助參考信號的定位方法實(shí)現(xiàn)心音信號的定位分段,首先使用歸一化香農(nóng)能量法提取心音信號的包絡(luò),然后再結(jié)合信號自身的特性準(zhǔn)確找出S1與S2的準(zhǔn)確位置。 特征分析、提取包含了信號時(shí)頻分析和功率譜估計(jì)。應(yīng)用了STFT、Choi-Williams分布這兩種時(shí)頻分析法來對比正常和異常人的心音信號之間的差別,然后采用了基于AR模型的參數(shù)譜估計(jì)法從時(shí)間、頻率和強(qiáng)度三個(gè)角度來對心音信號進(jìn)行特征提取。 提取到的特征使用SVM方法進(jìn)行識別診斷,對比了RBF、多項(xiàng)式函數(shù)和Sigmod函數(shù)構(gòu)建的分類器對識別效果的影響,通過實(shí)驗(yàn)發(fā)現(xiàn),RBF構(gòu)建的分類器的分類準(zhǔn)確率最高,可以達(dá)到76.7%。
[Abstract]:Congenital heart disease has had a great impact on human life and health, and the incidence of congenital heart disease has been high. According to incomplete statistics, there are about 1.5 million new congenital heart disease patients in the world every year. And nearly 60% of the patients can not get timely treatment, causing delay or even death, but if timely detection and treatment, most patients can return to normal. At present, all kinds of clinical diagnosis methods have certain defects, and the heart sound signal can show the pathological information of human body state change and heart disease change, so the digital signal processing method can be used to analyze the signal. To provide reference for clinical diagnosis. In this paper, we use MATLAB as the platform to analyze and study the heart sound signal from three aspects: signal preprocessing, feature analysis, extraction and recognition. The preprocessing includes noise reduction and segmental location. In this paper, the wavelet threshold denoising algorithm is first used to eliminate noise, and the effects of different thresholds and wavelet bases on the noise reduction are compared. Then the method of locating the heart sound signal without reference signal is used to realize the location segmentation of the heart sound signal. Firstly, the envelope of the heart sound signal is extracted by using the normalized Shannon energy method, and then the accurate position of S1 and S2 is found accurately according to the characteristics of the signal itself. Feature analysis, extraction includes signal time frequency analysis and power spectrum estimation. Two time-frequency analysis methods of STFT,Choi-Williams distribution are applied to compare the difference between normal and abnormal heart sound signals, and then the parameter spectrum estimation method based on AR model is used to estimate the time of heart sounds. The heart sound signal is extracted from three angles of frequency and intensity. The extracted features are identified and diagnosed by SVM method. The effect of the classifier constructed by RBF, polynomial function and Sigmod function on the recognition effect is compared. The experimental results show that the classifier constructed by RBF has the highest classification accuracy. It can reach 76. 7.
【學(xué)位授予單位】：云南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：R541.1;TN911.7

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本文編號：2313864