本文選題：呼吸音 + 特征提取�。� 參考：《西華大學(xué)》2015年碩士論文

【摘要】：呼吸疾病的機(jī)理信息在呼吸系統(tǒng)分布分散往往反應(yīng)在整個(gè)胸部區(qū)域,呼吸音聽診作為一種主要的呼吸音監(jiān)測(cè)手段,得到了越來(lái)越廣泛的應(yīng)用,但由于臨床常見呼吸音疾病多為混合病變,傳統(tǒng)的單導(dǎo)聽診只能依次反復(fù)聽診不同部位才能提供臨床信息,而多導(dǎo)呼吸音采集有利于在同一時(shí)間對(duì)不同部位間的呼吸音及雜音的持續(xù)時(shí)間、強(qiáng)度等信息進(jìn)行收集,提供豐富的臨床信息,并對(duì)采集的呼吸信號(hào)進(jìn)行進(jìn)一步分析研究,本文擬在以下幾個(gè)方面進(jìn)行深入研究:(1)熟悉呼吸音的基本信息,掌握呼吸音的產(chǎn)生機(jī)理以及呼吸雜音與呼吸系統(tǒng)疾病的病例關(guān)系和內(nèi)外的呼吸音的研究和方法。(2)分析與實(shí)驗(yàn)室有合作的山口大學(xué)自主研制的聽診頭是否符合呼吸音采集,此聽診頭主要由鐵三角AT9904麥克風(fēng)和3M公司生產(chǎn)的震動(dòng)腔(Littmann,ClassicIISE)構(gòu)成。(3)結(jié)合成都452醫(yī)院醫(yī)師指導(dǎo),提出五導(dǎo)采集部位,基于聽診部位和處理方法已經(jīng)申請(qǐng)一項(xiàng)實(shí)用新型專利目前已經(jīng)采用此方案采集到20名正常呼吸音志愿者的50多例呼吸音信號(hào)和20名患者的21例異常呼吸音信號(hào)(其中有14列相同病理),完成了初步的呼吸音采集測(cè)試。(4)對(duì)采集到的呼吸音信號(hào)進(jìn)行預(yù)處理分析,針對(duì)各種噪音設(shè)計(jì)相應(yīng)的處理方法,例如:工頻陷波器、小波閾值降噪、IIR濾波器設(shè)計(jì),通過(guò)以上的方案盡可能的提高呼吸音信號(hào)的信噪比,通過(guò)對(duì)比實(shí)驗(yàn),預(yù)處理效果比較明顯。(5)包絡(luò)提取與特征提取,針對(duì)呼吸音的特性,首先采用了頻域分析為輔,然后主要采用時(shí)域的包絡(luò)提取,采用常用包絡(luò)提取方法的對(duì)比,基于希爾伯特變換的呼吸音包絡(luò)提取、基于歸一化平均香農(nóng)能量的呼吸音包絡(luò)提取、基于單自由度模型的呼吸音包絡(luò)提取、基于Morlet小波呼吸音包絡(luò)提取的對(duì)比性試驗(yàn),選取Morlet包絡(luò)提取算法,然后通過(guò)采用FCM算法對(duì)包絡(luò)波進(jìn)行閾值線劃分,最后得到特征參數(shù)呼氣相和吸氣相的持續(xù)時(shí)間(T1,T2),呼氣間隙時(shí)間和吸氣間歇時(shí)間(D1,D2),呼氣相和吸氣相的峰值(P1,P2),以T1/T2,D1/D2作為區(qū)分呼吸音類型參數(shù),進(jìn)行初步的呼吸音二分類。(6)本實(shí)驗(yàn)室的最終目的在于能夠下位采集,后端有服務(wù)器的支持診斷,于是如何利用較少的資源去儲(chǔ)存和傳輸逐步增大的數(shù)據(jù)庫(kù)和提升單次采集數(shù)據(jù)傳輸速度,也是本文需要研究的一個(gè)方向,實(shí)驗(yàn)以改寫OMP算法和SAMP算法應(yīng)用于呼吸音,通過(guò)對(duì)兩種算法運(yùn)算過(guò)程中所需要涉及的步驟和條件進(jìn)行對(duì)比,得出需要在未知稀疏度(K)的前提下,改進(jìn)算法,加快運(yùn)算速度和運(yùn)算精準(zhǔn)度。綜上所述,本文對(duì)于呼吸音的采集和分析進(jìn)行了研究,并運(yùn)用實(shí)際采集到的臨床數(shù)據(jù)進(jìn)行驗(yàn)證,實(shí)驗(yàn)證明:五導(dǎo)呼吸音采集相對(duì)于單導(dǎo)聽診采集的優(yōu)勢(shì)明顯,能同時(shí)采集到數(shù)據(jù)后比較直觀的對(duì)不同部位的數(shù)據(jù)進(jìn)行對(duì)比和識(shí)別,基于FCM的聚類算法也能對(duì)呼吸音進(jìn)行較好的特征提取分類,而今后的工作重點(diǎn)將放在采集更多異常呼吸音和異常呼吸音的種類和呼吸音算法的優(yōu)化上。
[Abstract]:The mechanism of respiratory disease information in scattered respiratory system often in the chest area, respiratory auscultation as a main breathing monitoring means, has been more and more widely used, but because of clinical common breathing diseases are mixed lesions, single channel can only turn the traditional auscultation repeatedly in different parts in order to provide clinical auscultation and guide information, breathing is conducive to the acquisition at the same time the duration of breath sounds and murmurs among different parts of the strength, such as information collection, to provide clinical information rich, and the collection of respiratory signal for further analysis and research, this paper intends to conduct in-depth research in the following aspects: (1) familiar with the basic information of respiratory sounds, research and methods of respiratory sound master breath sound generating mechanism and respiratory murmur and respiratory diseases and abroad. (2) analysis The stethoscope head cooperate with laboratory developed by Yamaguchi University with respiratory sound collection, the stethoscope head is mainly composed of vibration cavity iron triangle AT9904 microphone and 3M company (Littmann, ClassicIISE). (3) Chengdu 452 hospital doctor guidance combined, put forward five guide collection parts, parts and processing method based on auscultation has been applied a utility model patent has been collected by using this method, more than 50 cases of 20 normal volunteers breathing respiratory sound signals and 20 patients with 21 cases of abnormal respiratory sounds (14 of the same pathological) completed respiratory sound acquisition, preliminary test. (4) pretreatment analysis of respiratory sounds the collected, processing method, the corresponding design for various noise such as frequency notch filter, wavelet threshold denoising, IIR filter is designed by the above scheme, as far as possible: the shouting signal sound The signal-to-noise ratio, through the contrast experiment, pretreatment effect is obvious. (5) envelope extraction and feature extraction, according to the characteristics of respiratory sounds, first used in the frequency domain analysis, and mainly uses the time domain envelope extraction, compared with the commonly used envelope extraction method, extraction of respiratory sound envelope based on Hilbert transform, extracting respiratory sound envelope the normalized average Shannon energy based on the extraction of respiratory sound envelope model of single degree of freedom based on the contrast test of extracting Morlet wavelet envelope based on respiration, select the Morlet envelope extraction algorithm, and then by using FCM algorithm to divide the threshold line envelope, and finally get the characteristic parameters of the expiratory phase and inspiratory phase duration (T1, T2), clearance time and expiratory inspiratory pause time (D1, D2), peak expiratory and inspiratory phase (P1, P2), T1/T2, D1/D2 as a division of respiratory sound type parameters, initial call Two sound classification. (6) the final purpose of this laboratory is able to support a collection, back-end diagnosis server, so how to use fewer resources to store and transfer to gradually increase the database and improve the single data transmission speed, also a direction of the study in this article, the experiment to rewrite the OMP algorithm and SAMP algorithm applied to the sound of breath through the steps and conditions involved need two kinds of algorithms in the process of comparison, found in unknown sparsity (K) algorithm under the premise, accelerate the operation speed and operation precision. To sum up, this paper does research on the collection and analysis of breath sounds, and verify the use of clinical data, the observed experimental results show: five guided breathing acquisition compared with single guide acquisition auscultation have obvious advantages, at the same time after collecting data more intuitive in different parts Comparing and identifying the data, the clustering algorithm based on FCM can also perform better feature extraction and classification of respiratory sounds, and the future work will focus on the collection of more abnormal breath sounds and the types of abnormal breathing sounds and the optimization of respiratory algorithm.

【學(xué)位授予單位】：西華大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：R443

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相關(guān)期刊論文 前1條

1 孫曉霞;劉曉霞;謝倩茹;;模糊C-均值(FCM)聚類算法的實(shí)現(xiàn)[J];計(jì)算機(jī)應(yīng)用與軟件;2008年03期

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本文編號(hào)：1761943