【摘要】：心音是人體重要的生理信號之一，能反映心臟及大血管的機械運動狀況，是臨床評估心臟功能狀態(tài)的最基本無創(chuàng)而便捷的方法。由于生命是最復(fù)雜的非線性動力系統(tǒng)，而心臟是生命循環(huán)系統(tǒng)的核心，這就決定了由心臟振動所產(chǎn)生的心音信號的非線性及復(fù)雜性。一直以來，人們?yōu)榱藢π呐K這個復(fù)雜的系統(tǒng)進行簡化及抽象，而對其建立一個理想的線性模型，并且用時域、頻域、時頻轉(zhuǎn)換等方法對該線性系統(tǒng)進行分析和處理。但是，半個世紀以來，人們發(fā)現(xiàn)用線性的方法進行分析并不足以研究本質(zhì)上為非線性的生命活動。由于混沌作為非線性系統(tǒng)的一種極為重要的運動形態(tài)，可以很好地揭示非線性過程內(nèi)在隨機性所具有的特殊規(guī)律性，從而本課題擬從混沌理論的角度對心音信號進行分析，從本質(zhì)上更深入地認識心音信號的內(nèi)在特征規(guī)律，，以期從一個全新的角度實現(xiàn)基于心音信號的心臟疾病的計算機輔助診斷。 為了提高心音信號的識別精度和分類準確性，采用小波包分析及混沌理論結(jié)合的方法對心音信號進行特征提取及分類識別。與小波變換相比，小波包具有更強的時頻分辨力，從而能夠提取原始信號局部更精細的時頻信息。一方面從時頻角度采用小波包對心音信號進行分析，利用小波包將心音信號分解成不同頻段，再對分解后的頻段作能量特征的提�。涣硗�，將小波包分解的心音信號分量中能表征心音信號特征的信號分解出來，對其進行混沌分析，包括定性及定量分析，其中定性分析包括心音信號相圖及遞歸圖，定量分析包括關(guān)聯(lián)維數(shù)、最大Lyapunov指數(shù)等混沌特征參量；然后將小波包分解的子帶能量特征和混沌特征參數(shù)結(jié)合構(gòu)成心音信號特征參數(shù)矢量，再通過遺傳算法分析心音信號小波包各頻帶能量特征以及混沌特征參數(shù)，選取了能夠表征心音信號的最優(yōu)特征矢量；最后采用支持向量機(SVM)作為分類器，以心音信號特征矢量作為輸入，從而實現(xiàn)心音信號的自動分類識別。 通過設(shè)計的心音信號采集系統(tǒng)，對臨床采集的正常及幾類異常心音信號，如早搏心律不齊、二尖瓣狹窄、第一心音分裂、主動脈關(guān)閉不全及室間隔缺損等異常心音，采用本文所述方法進行測試。結(jié)果表明，正常及異常心音信號的混沌定性定量特征都具有顯著性差異，其中異常心音信號的關(guān)聯(lián)維數(shù)及最大Lyapunov指數(shù)都較正常心音信號高，說明異常心音信號具有較高的復(fù)雜度。結(jié)合小波包能量及混沌特征的心音信號能夠獲得較高的識別率，說明混沌特征對于心音信號非線性特征的揭示具有重要的作用，為后期心音信號的診斷及心音非線性本質(zhì)的研究奠定了基礎(chǔ)。
[Abstract]:Heart sound is one of the important physiological signals of human body, which can reflect the mechanical movement of heart and large vessels. It is the most basic noninvasive and convenient method to evaluate the state of heart function in clinic. Life is the most complex nonlinear dynamic system, and the heart is the core of the circulatory system, which determines the nonlinearity and complexity of the heart sound signal produced by the heart vibration. In order to simplify and abstract the complex heart system, an ideal linear model has been established, and the linear system is analyzed and processed by time-domain, frequency-domain, time-frequency conversion and so on. However, for half a century, it has been found that linear analysis is not sufficient to study the essentially nonlinear activities of life. As a very important motion form of nonlinear system, chaos can well reveal the special regularity of the inherent randomness of nonlinear process, so this paper intends to analyze the heart sound signal from the point of view of chaos theory. In order to realize the computer-aided diagnosis of heart disease based on heart sound signal, we can deeply understand the inherent characteristics of heart sound signal in essence. In order to improve the recognition accuracy and classification accuracy of heart sound signal, the method of wavelet packet analysis and chaos theory is used to extract and classify the heart sound signal. Compared with the wavelet transform, the wavelet packet has stronger time-frequency resolution, so it can extract the local finer time-frequency information of the original signal. On the one hand, wavelet packet is used to analyze the heart sound signal from time-frequency angle, the heart sound signal is decomposed into different frequency bands by wavelet packet, and then the energy feature of the decomposed frequency band is extracted. In addition, the signal which can represent the characteristics of heart sound signal is decomposed from the component of heart sound signal decomposed by wavelet packet, and the chaotic analysis is carried out, including qualitative and quantitative analysis, in which qualitative analysis includes phase diagram and recursive diagram of heart sound signal. Quantitative analysis includes correlation dimension, maximum Lyapunov exponent and other chaotic characteristic parameters. Then the energy feature of the wavelet packet decomposition is combined with the chaotic characteristic parameter to form the characteristic parameter vector of the heart sound signal, and then the energy characteristics of each frequency band and the chaotic characteristic parameter of the heart sound signal wavelet packet are analyzed by genetic algorithm. The optimal feature vector which can represent the heart sound signal is selected. Finally, the support vector machine (SVM) is used as the classifier and the heart sound signal feature vector is used as the input to realize the automatic classification and recognition of the heart sound signal. The normal and several kinds of abnormal cardiac sound signals, such as premature beat arrhythmia, mitral stenosis, first heart sound division, aortic insufficiency and ventricular septal defect, were detected by the designed heart sound acquisition system. The method described in this paper is used for testing. The results showed that the chaotic qualitative and quantitative characteristics of normal and abnormal heart sounds were significantly different, and the correlation dimension and maximum Lyapunov index of abnormal heart sounds were higher than those of normal heart sounds. It shows that abnormal heart sound signal has high complexity. The combination of wavelet packet energy and chaotic characteristics can obtain a high recognition rate, which shows that chaotic features play an important role in revealing the nonlinear characteristics of heart sound signals. It lays a foundation for the diagnosis of heart sounds and the study of the nonlinear nature of heart sounds.
【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

【參考文獻】

相關(guān)期刊論文 前10條

1 王衍文,王海濱,程敬之;廣義時頻分析及其在PCG信號分析中的應(yīng)用[J];北京生物醫(yī)學(xué)工程;1998年02期

2 王興元,朱偉勇,顧樹生;利用關(guān)聯(lián)維數(shù)分析心臟的動力學(xué)特征[J];東北大學(xué)學(xué)報;1999年06期

3 朱冰蓮;呂佶;;時頻表達在心音研究中的應(yīng)用[J];重慶大學(xué)學(xué)報(自然科學(xué)版);2006年05期

4 張雨;任成龍;;確定重構(gòu)相空間維數(shù)的方法[J];國防科技大學(xué)學(xué)報;2005年06期

5 楊志安，王光瑞，陳式剛;用等間距分格子法計算互信息函數(shù)確定延遲時間[J];計算物理;1995年04期

6 呂振環(huán),吳素文,李喜霞;論混沌學(xué)的發(fā)展、特性及其意義[J];沈陽農(nóng)業(yè)大學(xué)學(xué)報(社會科學(xué)版);2004年01期

7 胡睿;陳惠;李曉雅;王天鵝;;慢病已成為中國不可承受之重[J];中國社區(qū)醫(yī)師;2011年32期

8 林嘉宇,王躍科,黃芝平,沈振康;語音信號相空間重構(gòu)中時間延遲的選擇──復(fù)自相關(guān)法[J];信號處理;1999年03期

9 嚴勇,董筠,樓正國;心音的非線性時間序列分析[J];中國醫(yī)學(xué)物理學(xué)雜志;2004年03期

10 王興元,顧樹生;心電動態(tài)生理及病理信息的非線性動力學(xué)研究[J];中國生物醫(yī)學(xué)工程學(xué)報;2000年04期

相關(guān)會議論文 前1條

1 丁曉蓉;宋陸州;包鑫;郭興明;;心音采集及其混沌動力學(xué)分析方法的初步研究[A];中國電子學(xué)會第十七屆信息論學(xué)術(shù)年會論文集[C];2010年

相關(guān)碩士學(xué)位論文 前6條

1 董筠;心音的非線性時間序列分析[D];浙江大學(xué);2003年

2 王恒迪;非線性動力學(xué)在房顫研究中的應(yīng)用[D];北京工業(yè)大學(xué);2003年

3 周靜;心音信號分析方法的研究及其分析系統(tǒng)的開發(fā)[D];重慶大學(xué);2004年

4 姜桂仁;混沌時序的特征量分析及相空間重構(gòu)[D];江蘇大學(xué);2005年

5 王丹丹;混沌在心電數(shù)據(jù)分析中的應(yīng)用[D];中國海洋大學(xué);2006年

6 吳玉春;心音信號自動識別算法的研究[D];重慶大學(xué);2009年

本文編號：2399848