發(fā)布時間：2018-03-05 16:34

  本文選題：心電分析　切入點：經(jīng)驗模式分解　出處：《天津工業(yè)大學》2017年碩士論文　論文類型：學位論文

【摘要】：近年來,隨著人們物質生活水平的提高,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,因此準確而及時的檢測出患者心律失常類型,對預防心血管疾病具有及其重要的意義。心律失常分類技術是心電信號自動分析領域的重點研究內容,但由于其心電信號個體差異和易受噪聲干擾的特點,要實現(xiàn)準確的特征提取及分類仍然存在一些難題。基于此,本課題針對心電信號特征提取及心律失常的分類進行了研究,本文的主要工作內容如下:1.心電信號預處理。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計了中值濾波器及小波軟閾值濾波器,并通過實驗仿真驗證,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。2.心電信號特征提取。為了更加準確全面的表征心電信號的本質特征,本文提出了時域特征和變換域非線性特征相結合的方法。時域上通過經(jīng)驗模式分解和差分閾值相結合的方法提取了 QRS波群特征點,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)驗模式分解及近似熵相結合的方法,通過對其前六個本證模態(tài)函數(shù)近似熵的計算,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)心電信號準確分類奠定基礎。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)勢的支持向量機分類模型對正常心電及四種常見心律失常信號進行分類處理。并針對標準粒子群參數(shù)優(yōu)化算法在實際應用中易陷入局部最優(yōu)的缺點,提出了改進的粒子群參數(shù)尋優(yōu)算法。并綜合利用改進的粒子群優(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,并利用改進的粒子群優(yōu)化的支持向量機實現(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)庫進行仿真驗證表明,本文算法能夠實現(xiàn)心電節(jié)拍的準確分類,對心律失常診斷分析具有一定的現(xiàn)實意義,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.
【學位授予單位】：天津工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R541.7;TN911.7

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