發(fā)布時間：2018-03-05 23:32

  本文選題：心音包絡　切入點：歸一化香農(nóng)能量　出處：《浙江大學》2015年碩士論文　論文類型：學位論文

【摘要】：心腦血管疾病是近年來發(fā)病率較高的疾病之一,不僅難以預防,而且致死率高,是人們特別是老年人的頭號殺手,而心臟類疾病又是心血管疾病里比較常見的疾病�，F(xiàn)有的檢測心臟疾病的手段比較多,比如心電圖、B超以及彩超等,但此類檢測手段只能在心臟出現(xiàn)明顯病變后才能檢測出問題來,不利于疾病的盡早發(fā)現(xiàn)和治療。另外一種檢測手段——心音聽診,可以在心臟出現(xiàn)明顯病變之前就發(fā)現(xiàn)相關的病變信息,但是心音聽診沒有統(tǒng)一的客觀評價標準,只能依賴于醫(yī)生的主觀經(jīng)驗。因此,本文結合心音的生理學特點,通過心音信號包絡提取方法和分段方法,提取相關的醫(yī)學指標參數(shù),嘗試為臨床診斷提供一些客觀評價依據(jù)。本文研究了不同的心音信號包絡提取方法和分段方法,提取了一些醫(yī)學指標參數(shù),開發(fā)了心音分段圖形用戶界面,主要的工作內(nèi)容如下：1.對心音信號進行了預處理。使用重采樣對心音信號進行處理以減少數(shù)據(jù)量,減輕后續(xù)數(shù)據(jù)處理的壓力；使用五階巴特沃斯帶通濾波器濾除心音信號中的高頻和低頻噪聲；使用自適應小波去噪濾除部分頻率與心音信號頻率有重疊的噪聲；使用歸一化對心音信號的強度進行統(tǒng)一。2.提出了一種將歸一化香農(nóng)能量方法與希爾伯特黃變換方法相結合的心音包絡提取方法。首先介紹了兩種常用的包絡提取方法,即歸一化香農(nóng)能量方法和希爾伯特黃變換方法,并通過實驗對其優(yōu)缺點進行了比較。為了彌補歸一化香農(nóng)能量方法和希爾伯特黃變換方法的一些不足,本文提出了一種將歸一化香農(nóng)能量方法和希爾伯特黃變換方法相結合的心音包絡提取方法,在該方法中,使用鏡像閉合延拓方法對端點飛翼問題進行了解決。實驗表明,新方法能夠得到較好的包絡曲線。3.對心音信號進行了分段并提取了相關的醫(yī)學指標參數(shù)。首先對單閾值分段方法及其優(yōu)缺點進行了介紹,為了彌補單閾值分段方法的不足,本文對雙閾值分段方法進行了研究。實驗表明,雙閾值分段方法較之單閾值方法具有更好的分段效果。其次介紹了一些心音相關的醫(yī)學指標參數(shù),并針對分段后的心音信號進行了醫(yī)學指標參數(shù)的提取。4.開發(fā)了心音分段圖形用戶界面。對圖形用戶界面的一般設計原則和制作步驟進行了介紹,并且開發(fā)了心音分段圖形用戶界面。使用上述三種包絡提取方法和雙閾值分段方法,本文對包括309個第一心音和304個第二心音在內(nèi)的30例心音信號進行了分析測試,實驗結果表明,對歸一化香農(nóng)能量包絡進行分段,第一心音和第二心音的檢出率分別為84.47%和84.54%；對希爾伯特包絡進行分段,第一心音和第二心音的檢出率分別為74.43%和72.04%；對新包絡提取方法得到的包絡曲線進行分段,第一心音和第二心音的檢出率分別為91.59%和90.79%。對比實驗結果可知,新包絡提取方法能夠獲得最好的分段效果。
[Abstract]:Cardio-cerebrovascular disease is one of the diseases with high incidence in recent years. It is not only difficult to prevent, but also has a high mortality rate. It is the number one killer of people, especially the elderly. And heart disease is a common disease in cardiovascular disease. There are more methods available to detect heart disease, such as electrocardiogram (ECG), B-mode ultrasound (B-ultrasound) and color Doppler ultrasound (CDUs), etc. But this kind of detection method can detect the problem only after the heart has obvious pathological changes, which is not conducive to the early detection and treatment of the disease. We can find the relevant pathological information before the obvious pathological changes in the heart, but there is no unified objective evaluation standard in the auscultation of heart sounds, which can only depend on the subjective experience of the doctors. Therefore, this paper combines the physiological characteristics of heart sounds. In this paper, the envelope extraction method and segmentation method of heart sound signal are used to extract the relevant medical index parameters and to provide some objective evaluation basis for clinical diagnosis. This paper studies different methods of heart sound signal envelope extraction and segmentation. Some medical index parameters are extracted, and the heart sound segmentation graphical user interface is developed. The main work is as follows: 1. The heart sound signal is preprocessed. The heart sound signal is processed by resampling in order to reduce the amount of data. To reduce the pressure of the subsequent data processing, the fifth order Butterworth bandpass filter is used to filter the high frequency and low frequency noise in the heart sound signal, and the adaptive wavelet denoising method is used to filter the noise with overlapping frequency of the part frequency and the heart sound signal frequency. The intensity of heart sound signal is unified by normalization. 2. A new method of extracting heart sound envelope is proposed, which combines normalized Shannon energy method with Hilbert yellow transform method. Firstly, two kinds of commonly used envelope extraction methods are introduced. That is, normalized Shannon energy method and Hilbert-Huang transform method, and their advantages and disadvantages are compared through experiments, in order to make up for some shortcomings of normalized Shannon energy method and Hilbert Huang transform method. In this paper, a method of extracting heart sound envelope by combining normalized Shannon energy method with Hilbert-Huang transform method is proposed. In this method, the problem of terminal flying wing is solved by image closed continuation method. The new method can get a better envelope curve .3.The heart sound signal is segmented and the relevant medical index parameters are extracted. Firstly, the single threshold segmentation method and its advantages and disadvantages are introduced, in order to make up for the shortcomings of the single threshold segmentation method. In this paper, the double threshold segmentation method is studied. The experimental results show that the double threshold segmentation method has better segmentation effect than the single threshold method. Secondly, some medical parameters related to heart sounds are introduced. At the same time, the medical index parameters of the segmented heart sound signal are extracted. 4. The segmentation graphical user interface of heart sound is developed. The general design principles and the manufacturing steps of the graphical user interface are introduced. Using the above three envelope extraction methods and two threshold segmentation methods, 30 heart sound signals, including 309 first heart sounds and 304 second heart sounds, were analyzed and tested. The experimental results show that the detection rates of the normalized Shannon energy envelope are 84.47% and 84.54 respectively for the first heart sound and the second heart sound, and the Hilbert envelope is segmented. The detection rates of first heart sound and second heart sound were 74.43% and 72.04 respectively. The new envelope extraction method can obtain the best segmentation effect.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：R54;TN912.3

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