心電信號特征提取及心律失常分類算法研究
發(fā)布時間：2018-03-05 16:34
本文選題：心電分析 切入點(diǎn)：經(jīng)驗(yàn)?zāi)Ｊ椒纸?/strong> 出處：《天津工業(yè)大學(xué)》2017年碩士論文 論文類型：學(xué)位論文
【摘要】：近年來,隨著人們物質(zhì)生活水平的提高,心血管疾病的發(fā)病率和死亡率逐年增加,并呈現(xiàn)出明顯的年輕化趨勢。而心血管疾病患者早期癥狀往往都伴隨著心律失�，F(xiàn)象,因此準(zhǔn)確而及時的檢測出患者心律失常類型,對預(yù)防心血管疾病具有及其重要的意義。心律失常分類技術(shù)是心電信號自動分析領(lǐng)域的重點(diǎn)研究內(nèi)容,但由于其心電信號個體差異和易受噪聲干擾的特點(diǎn),要實(shí)現(xiàn)準(zhǔn)確的特征提取及分類仍然存在一些難題�；诖�,本課題針對心電信號特征提取及心律失常的分類進(jìn)行了研究,本文的主要工作內(nèi)容如下:1.心電信號預(yù)處理。本文分別針對心電信號中常見的低頻基線漂移噪聲及高頻干擾噪聲設(shè)計(jì)了中值濾波器及小波軟閾值濾波器,并通過實(shí)驗(yàn)仿真驗(yàn)證,選取合適的窗口長度及小波基,較好的保留了原始信號的波形特點(diǎn)。2.心電信號特征提取。為了更加準(zhǔn)確全面的表征心電信號的本質(zhì)特征,本文提出了時域特征和變換域非線性特征相結(jié)合的方法。時域上通過經(jīng)驗(yàn)?zāi)Ｊ椒纸夂筒罘珠撝迪嘟Y(jié)合的方法提取了 QRS波群特征點(diǎn),選取了 RR間期,心率變異性及QRS波群時限長度作為時域特征向量。利用經(jīng)驗(yàn)?zāi)Ｊ椒纸饧敖旗叵嘟Y(jié)合的方法,通過對其前六個本證模態(tài)函數(shù)近似熵的計(jì)算,得到了心電信號變換域非線性特征。將兩組特征融合作為分類特征向量集,為后續(xù)心電信號準(zhǔn)確分類奠定基礎(chǔ)。3.心律失常分類。綜合比較幾種常見分類器性能,選取對小樣本非線性分類問題具有絕對優(yōu)勢的支持向量機(jī)分類模型對正常心電及四種常見心律失常信號進(jìn)行分類處理。并針對標(biāo)準(zhǔn)粒子群參數(shù)優(yōu)化算法在實(shí)際應(yīng)用中易陷入局部最優(yōu)的缺點(diǎn),提出了改進(jìn)的粒子群參數(shù)尋優(yōu)算法。并綜合利用改進(jìn)的粒子群優(yōu)化算法尋求最優(yōu)參數(shù),提高了分類的可靠性。綜上所述,本文利用時域特征及其變換域非線性特征融合的特征向量集來表征心電信號,并利用改進(jìn)的粒子群優(yōu)化的支持向量機(jī)實(shí)現(xiàn)了常見心律失常信號的分類。通過MIT-BIH心律失常數(shù)據(jù)庫進(jìn)行仿真驗(yàn)證表明,本文算法能夠?qū)崿F(xiàn)心電節(jié)拍的準(zhǔn)確分類,對心律失常診斷分析具有一定的現(xiàn)實(shí)意義,可用于心電分析輔助診斷。
[Abstract]:In recent years, with the improvement of people's material standard of living, the morbidity and mortality of cardiovascular diseases have been increasing year by year, and the trend of younger age is obvious. The early symptoms of patients with cardiovascular diseases are often accompanied by arrhythmia. Therefore, accurate and timely detection of patients' arrhythmia types is of great significance in preventing cardiovascular disease. Arrhythmia classification technology is an important research content in the field of ECG automatic analysis. However, due to the individual difference of ECG signal and its characteristics of being susceptible to noise interference, there are still some difficulties in the realization of accurate feature extraction and classification. Based on this, this paper studies ECG feature extraction and arrhythmia classification. The main work of this paper is as follows: 1. ECG signal preprocessing. In this paper, median filter and wavelet soft threshold filter are designed for low frequency baseline drift noise and high frequency interference noise respectively. Selecting the appropriate window length and wavelet base, the waveform characteristics of the original signal are better preserved. 2. ECG signal feature extraction. In order to more accurately and comprehensively characterize the essential characteristics of ECG signal, In this paper, a method of combining time domain features with transform domain nonlinear features is proposed. In time domain, QRS wave group feature points are extracted by empirical mode decomposition and difference threshold method, RR interval is selected. Heart rate variability (HRV) and the time limit of QRS wave group are used as time domain Eigenvectors. Using the method of empirical mode decomposition and approximate entropy, the approximate entropy of the first six intrinsic modal functions is calculated. The nonlinear features of ECG signal transform domain are obtained. The fusion of two groups of features as the classification feature vector set lays a foundation for accurate classification of ECG signals. 3. Arrhythmia classification. The performance of several common classifiers is compared synthetically. The support vector machine (SVM) classification model, which is superior to the small sample nonlinear classification problem, is selected to classify normal ECG and four kinds of common arrhythmia signals. The standard particle swarm optimization algorithm is applied in practice. It is easy to fall into local optimum in use, An improved particle swarm optimization algorithm is proposed, and the improved particle swarm optimization algorithm is used to find the optimal parameters, which improves the reliability of the classification. In this paper, the feature vector set of time domain feature and its transform domain nonlinear feature fusion is used to represent ECG signal. The classification of common arrhythmia signals is realized by using improved particle swarm optimization support vector machine. The simulation results of MIT-BIH arrhythmia database show that the proposed algorithm can achieve accurate classification of ECG beats. The diagnosis and analysis of arrhythmias have certain practical significance and can be used in ECG analysis to assist diagnosis.
【學(xué)位授予單位】：天津工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R541.7;TN911.7
【參考文獻(xiàn)】
相關(guān)期刊論文 前7條
1 隋輝;陳偉偉;王文;;《中國心血管病報告2015》要點(diǎn)解讀[J];中國心血管雜志;2016年04期
2 趙勇;洪文學(xué);孫士博;;基于多特征和支持向量機(jī)的心律失常分類[J];生物醫(yī)學(xué)工程學(xué)雜志;2011年02期
3 童佳斐;董軍;;分類器組合在心電圖分類中的應(yīng)用[J];計(jì)算機(jī)應(yīng)用;2010年04期
4 李學(xué)華;莫智文;舒蘭;;KPL特征提取在心電識別中的應(yīng)用研究[J];微計(jì)算機(jī)信息;2009年27期
5 行鴻彥;黃敏松;;基于Hilbert-Huang變換的QRS波檢測算法研究[J];儀器儀表學(xué)報;2009年07期
6 劉雄飛;黃茁;;基于小波神經(jīng)網(wǎng)絡(luò)的心電診斷算法的研究[J];計(jì)算機(jī)仿真;2009年04期
7 吳杰，陸再英;常規(guī)心電圖描記分析方法標(biāo)準(zhǔn)化的進(jìn)展[J];中華心血管病雜志;1995年01期
，本文編號：1571046
本文鏈接：http://sikaile.net/yixuelunwen/xxg/1571046.html
最近更新
教材專著
