The Study Of The Noninvasive Diagnosis Of Coronary Artery Disease Based On The Analysis And Features Extraction Of Diastolic Murmurs

Coronary Artery Disease (CAD) is one of the leading causes of death in the world. In recent 10 years, with the raising of life quantity and the extending of the average longevity, Morbidity for CAD is gradually increasing with the trend of patients being young. CAD is harmful with the characteristic of Severity popularity. and complexity. Seasonable diagnosis and cure are important. Developing the study of the noninvasive detection of CAD is essential. Detection of CAD is a most important medical research area. Heart sound is one of the most physiological signals of body. It provides a lot of valuable diagnostic and prognostic information concerning the heart and hemodynamics. It had important potentiality in clinical practice. Combination the traditional phonocrdiogrm (PCG) with the modern signal processing may bring the breakthrough in the noninvasive detection of CAD.This paper mainly concerns the diagnosis of CAD automatically based on the analysis and features extraction of diastolic murmurs. On the basis of analyzing the relation between the coronary artery block and the diastolic murmurs, firstly this paper studied the preprocessing of PCG (denoising and segmentation), the diastolic periods were positioned; secondly the diastolic murmurs was analyzed by use of HHT, The features of CAD were acquired; In the end, the CAD was diagnosed automatically by use of Support Vector Machine (SVM), which was suitable in the situation of small samples and better diagnosis ratio was acquired.The main contributions of this thesis are as follows:The denoising of biomedical signal was studied. The generalized frame of wavelet shrinkage denoising was build. The generalized threshold function was proposed and the threshold value was also studied for the generalized threshold function. Efficient formulas for computing bias, variance and risk of generalized threshold function were derived. On the basis of this, the relation of bias, variance and risk of generalized threshold function with threshold value and wavelet coefficient were compared. These comparisons gave the performances of wavelet shrinkage denoising in finite sample situations. Kinds of signals with different features were considered to illustrate the performances of the different threshold functions and different threshold values. The optimal denoising scheme was achieved. . In end, the method was used to denoise the electrocardiogram signal and phonocardiogram signal, and the better performance was acquired.On the basis of reviewing the virtues and defects of traditional PCG segmentation algorithms, a novel segmentation algorithm was proposed. The steps and strategies of segmentation were given. The algorithm was tested using a lot of clinic normal and abnormal heart sound data. Whereas the heart sound signals were intricate, the algorithm achieved a high detection rate. The correct detection rate wasvery high by the segmentation algorithm and the algorithm was robust without other signal as reference.HHT method was studied. Traditional signal processing methods were reviewed; the instantaneous frequency of multicomponent signal was further researched. The concept of intrinsic mode function was induced by the existing conditions of instantaneous frequency for multicomponent signal. The principles of HHT were introduced, including IMF, EMD Hilbert spectrum and marginal spectrum. Some factors related the performances of HHT were studied, and the methods were proposed to improve the performance. The methods were very important for the applications of HHT. In the end, some kinds of signals were analyzed by HHT; the result indicated the HHT method had better qualities.The diastolic murmurs was analyzed by HHT. The methods of analyzing diastolic murmurs and the features obtained by HHT were presented. The adaptive linear enhancer was designed to improve the signal noise ration of diastolic murmurs. The stationarity of diastolic murmurs was analyzed. The instantaneous frequency and Hilbert spectrum and marginal spectrum of diastolic murmurs for CAD...