【摘要】：先天性心臟病(簡稱先心病)屬于出生缺陷疾病。通過手術(shù)即可治愈,并且早發(fā)現(xiàn)、早治療將大大提高其治愈率。但先心病的早期癥狀不明顯,其診斷分為初診和確診,初診階段主要是聽診,易受主觀經(jīng)驗影響而出現(xiàn)漏診,耽誤治療的黃金時間。心音圖含有大量的心臟信息,對心音圖進行數(shù)字信號處理,可大大改善這一情況,提高疾病的診斷效率。 本文從心音的產(chǎn)生機制、信號時頻特性,以及基本的心音處理流程出發(fā),包括預(yù)處理、心音分割、特征提取,對心音信號進行了定量和定性分析。 在預(yù)處理階段,提出了針對心音分析的簡化小波包多分辨率分解算法,得到了心音能量在不同頻率段的分布,有效地將噪聲與心音、生理及病理雜音分離開后；利用心音與雜音的頻率分布關(guān)系,定性地將各小波包系數(shù)劃分為超低、低、中和高頻4個頻段,并使用歸一化香濃能量包絡(luò)提取算法,來計算上述4個頻段的信號包絡(luò)；然后根據(jù)自相關(guān)相關(guān)原理,通過相乘與相加對這四個包絡(luò)信號進行突出與壓縮,得到心音包絡(luò)和標(biāo)尺包絡(luò)。其中,心音包絡(luò)具有信號的總體包絡(luò)信息,而標(biāo)尺包絡(luò)對能量較強的奇異點進行了放大,對能量較弱的毛刺進行了削弱。 在心音分割階段,本文提出了新的包絡(luò)提取策略,利用心音包絡(luò)和標(biāo)尺包絡(luò),結(jié)合心音基本時域特征,實現(xiàn)了心音的自適應(yīng)分段定位,且無需心電等其他參考信息。該方法從時域和頻域兩大角度出發(fā),得到的包絡(luò)信息較完整。對正常和異常共計50例復(fù)雜心音的定位分段準(zhǔn)確率達95%以上。新的方法不僅效率高,同時有效地降低了傳統(tǒng)心音分段方法僅從峰值點處左右移動固定長度,來計算心音邊界位置而存在的偏移風(fēng)險,減少誤判等錯誤的出現(xiàn)。 在特征提取方面,本文進行了雜音檢測和心力儲備評估兩方面的心音特征提取,將郭興明教授提出的心臟儲備指標(biāo)(S1/S2、D/S、HR)作為先心病無創(chuàng)檢測的輔助指標(biāo),發(fā)現(xiàn)異常心音的D/S評級較低,HR偏高。此外,雜音的出現(xiàn)是先心病最主要的病癥,為此本文計算了收縮期、舒張期各期能量分數(shù),發(fā)現(xiàn)在中、高頻段早、中、晚各期的能量分數(shù)中,異常心音要高于正常心音,這有力地提示了病理雜音的出現(xiàn)；其次,發(fā)現(xiàn)低頻段生理雜音較多,且容易與該頻段的病理雜音混淆,同時正常的第三心音和第四心音,容易與舒張期中、晚期雜音混淆,至此,本文從頻率和能量的角度,對正常、異常,以及不同疾病程度的50例心音進行研究,提取到了有效的心音信號特征,為臨床心音分析應(yīng)用的開發(fā)打下了基礎(chǔ)。
[Abstract]:Congenital heart disease (CHD) is a birth defect disease. Surgical treatment can be cured, and early detection, early treatment will greatly improve its cure rate. But the early symptoms of congenital heart disease are not obvious, its diagnosis is divided into initial diagnosis and diagnosis, the first diagnosis stage is mainly auscultation, easy to be affected by subjective experience and missed diagnosis, delaying the prime time of treatment. Cardiac phonogram contains a lot of heart information. Digital signal processing can greatly improve this situation and improve the diagnosis efficiency of disease. In this paper, the mechanism of heart sound generation, the time-frequency characteristic of the signal, and the basic flow of heart sound processing, including pretreatment, heart sound segmentation, feature extraction, and quantitative and qualitative analysis of heart sound signal are analyzed quantitatively and qualitatively. In the preprocessing stage, a simplified wavelet packet multi-resolution decomposition algorithm for heart sound analysis is proposed. The distribution of heart sound energy in different frequency bands is obtained, and the noise is effectively separated from heart sound, physiological and pathological murmur. According to the frequency distribution of heart sound and murmur, the wavelet packet coefficients are qualitatively divided into four bands: ultra low, middle and high frequency, and the normalized energy envelope extraction algorithm is used to calculate the signal envelope of the above four bands. According to the autocorrelation principle, the four envelope signals are extruded and compressed by multiplying and adding, and the heart sound envelope and the scale envelope are obtained. The heart sound envelope has the total envelope information of the signal, while the scale envelope amplifies the singularity point with stronger energy and weakens the burr with weaker energy. In the phase of heart sound segmentation, a new envelope extraction strategy is proposed in this paper. With the use of heart sound envelope and scale envelope, combined with the basic characteristics of heart sound in time domain, the adaptive segmentation of heart sound is realized, and other reference information such as ECG are not required. The envelope information obtained by this method is relatively complete from time domain and frequency domain. The accuracy of segmental localization of 50 cases of normal and abnormal heart sounds was over 95%. The new method not only has high efficiency, but also effectively reduces the shift of fixed length from the peak point to the left and right in the traditional heart sound segmentation method, so as to calculate the deviation risk of the heart sound boundary position and reduce the occurrence of errors such as misjudgment. In the aspect of feature extraction, the murmur detection and assessment of cardiac force reserve were carried out in this paper. The cardiac reserve index (S1 / S2D / SHR) proposed by Professor Guo Xingming was used as the auxiliary index for noninvasive detection of congenital heart disease. The D / S rating of abnormal heart sounds was lower and HR was higher. In addition, the emergence of murmur is the most important disease of congenital heart disease. Therefore, the energy fractions of systolic and diastolic phases are calculated. It is found that abnormal heart sounds are higher than normal heart sounds in the early, middle and late stages of middle, high frequency. This strongly indicates the appearance of pathological murmur; Secondly, we found that there are many physiological murmur in low frequency band, and it is easy to be confused with pathological murmur in this frequency band. At the same time, the normal third heart sound and fourth heart sound are easily confused with middle and late diastolic murmur. So far, from the angle of frequency and energy, 50 cases of heart sounds with normal, abnormal and different degree of disease were studied, and the effective characteristics of heart sounds were extracted, which laid a foundation for the development of clinical analysis and application of heart sounds.
【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】：R725.4;TN911.7

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