本文選題：心電信號　切入點(diǎn)：小波變換　出處：《中南大學(xué)》2014年碩士論文

【摘要】：心律失常在人們的生活中比較尋常,心律失常的發(fā)生可能會對人的生命造成影響,因此,為了預(yù)防心律失常的發(fā)生,我們必須更加準(zhǔn)確和及時的對心律失常進(jìn)行檢測。 近年來,利用計(jì)算機(jī)對心律失常進(jìn)行自動處理已成趨勢,但是由于心電信號的電流微小,受到的干擾較多,同時,由于個人的區(qū)別和心律失常分類的規(guī)則不統(tǒng)一等緣故,計(jì)算機(jī)對心臟的診斷至今難以滿足醫(yī)院的需求。本文針對這一狀況,在前人的基礎(chǔ)上對常見的六種心律失常進(jìn)行識別,主要的工作如下： 心電信號預(yù)處理：使用小波變換的分解重構(gòu)法去除信號中的基線漂移,使用小波變換的閥值法去除高頻肌電干擾和工頻干擾,本文結(jié)合小波變換的分解重構(gòu)法和小波變換的閥值法既可以消除信號中的主要噪聲干擾,又可以避免有用成分丟失。通過美國麻省理工學(xué)院和貝絲以色列醫(yī)院(Massachusettes Institute of Technology and Beth Israel Hospital, MIT-BIH)心律失常數(shù)據(jù)庫中的數(shù)據(jù)仿真可知,達(dá)到了較好的效果。 心電信號QRS波群的檢測：針對目前的Mexican-hat小波檢測法,由于心律失常的影響,容易造成低幅R波漏檢,同時由于高大P波、T波和高頻噪聲的影響,容易造成R波誤檢,因此,本文采用連續(xù)小波變換和多種策略的方法來檢測QRS波群,通過MIT-BIH數(shù)據(jù)庫仿真,檢測正確率達(dá)99.50%。 心電特征參數(shù)的提取與選擇：由于傳統(tǒng)的特征提取只考慮時域特征,其具有一定片面性,不足以反映心電信號的本質(zhì),為了更加準(zhǔn)確的反映心電信號的本質(zhì)特征,本文綜合采用時域特征和小波域特征作為心電信號的特征向量,時域上提取了RR1、RR2、QRS波寬和心率變異性(Heart rate variability, HRV)四個特征,小波域上提取了第四層尺度信號,第四層小波信號和第三層小波信號,然后對特征向量進(jìn)行了優(yōu)化,為后續(xù)分類奠定了良好的基礎(chǔ)。 心律失常的分類：設(shè)計(jì)一種支持向量機(jī)(Library for Support Vector Machines, LIB SVM)分類器,對優(yōu)化后的特征向量進(jìn)行訓(xùn)練和測試,然后對MIT-BIH心律失常數(shù)據(jù)庫中的六種典型的心律失常類型進(jìn)行分類,分類的整體正確率達(dá)到96.60%以上。
[Abstract]:Arrhythmia is more common in people's life, the occurrence of arrhythmia may affect human life. Therefore, in order to prevent the occurrence of arrhythmia, we must more accurately and timely detection of arrhythmia.In recent years, it has become a trend to use computer to process arrhythmia automatically, but because of the small current of ECG signal, the disturbance is more, at the same time, because of the difference of individual and the rule of arrhythmia classification, etc.The diagnosis of the heart by computer is still difficult to meet the needs of the hospital.In this paper, six common arrhythmias are identified on the basis of previous studies. The main work is as follows:ECG signal preprocessing: wavelet transform decomposition reconstruction method is used to remove baseline drift, wavelet transform threshold method is used to remove high frequency EMG interference and power frequency interference.In this paper, the decomposition reconstruction method of wavelet transform and the threshold value method of wavelet transform can not only eliminate the main noise interference in the signal, but also avoid the loss of useful components.Through the data simulation of Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) arrhythmia database of Massachusetts Institute of Technology and Beth Israel Hospital, the results are satisfactory.Detection of QRS wave group in ECG signals: for the current Mexican-hat wavelet detection method, it is easy to cause low amplitude R wave miss detection due to arrhythmia, at the same time, because of the influence of high P wave T wave and high frequency noise, it is easy to cause R wave false detection.In this paper, continuous wavelet transform and multiple strategies are used to detect QRS wave groups, and the correct detection rate is 99.50 by MIT-BIH database simulation.Extraction and selection of ECG feature parameters: because traditional feature extraction only considers time domain feature, it has certain one-sidedness, which is not enough to reflect the essence of ECG signal, in order to reflect the essential characteristics of ECG signal more accurately.In this paper, the time domain feature and the wavelet domain feature are used as the feature vectors of ECG signal. The RR1 / RR2 QRS wave width and heart rate variability (HRV) are extracted from the time domain, and the fourth layer scale signal is extracted in the wavelet domain.The fourth layer wavelet signal and the third layer wavelet signal are optimized, which lays a good foundation for the subsequent classification.Classification of arrhythmias: a support vector machine library for Support Vector machines (LIB SVM) classifier is designed to train and test the optimized feature vectors, and then classify six typical arrhythmia types in MIT-BIH arrhythmia database.The overall correct rate of classification is above 96.60%.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.7;O174.2

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本文編號：1710382