本文選題：心電信號　切入點：特征提取　出處：《天津工業(yè)大學》2017年碩士論文

【摘要】：心電圖是人體心臟電活動最直接的反映,是醫(yī)生進行心臟病診斷治療的重要依據(jù)之一。隨著科學和技術的發(fā)展,基于心電圖的自動分析診斷技術已被廣泛地用于心臟病檢測和診斷的研究�；谛碾妶D的自動分析診斷技術不僅大大降低了醫(yī)生的工作量,而且可以顯著地提高心電圖分類的效率和準確率,對于心臟病及時的診斷和治療具有重要的實際應用價值。因此,本文主要針對心電自動分析診斷技術中的心電信號分類識別方法進行了深入的研究,主要研究內(nèi)容包括心電信號的特征提取和特征分類。提取穩(wěn)定有效的心電信號特征是心電自動分析診斷技術中的重要環(huán)節(jié),本文提出了一種基于小波包分解與統(tǒng)計分析相結(jié)合的心電信號特征提取算法。該算法首先采用小波包分解方法對心電信號進行四尺度分解,然后結(jié)合統(tǒng)計分析方法計算小波包分解后第四尺度上的16個小波包系數(shù)的奇異值、標準差和最大值,將求得的48維小波包系數(shù)統(tǒng)計特征組成心電信號特征空間。為了盡可能地提高心電信號分類識別的效率和準確率,本文提出了一種基于遺傳算法優(yōu)化神經(jīng)網(wǎng)絡的心電信號特征選擇和分類算法。通過遺傳算法對心電信號特征空間進行降維得到25維心電信號特征,同時采用遺傳算法對誤差反向傳播神經(jīng)網(wǎng)絡分類器的權(quán)值和閾值進行優(yōu)化,將降維得到的心電信號特征輸入到分類器中進行訓練和預測,從而實現(xiàn)對MIT-BIH心律失常數(shù)據(jù)庫中六類心電信號:正常心跳、左束支傳導阻滯、右束支傳導阻滯、起搏心跳、室性早搏和房性早搏的分類,測試集的識別準確率為97.78%,平均靈敏度、平均特異度和平均陽性預測值分別為97.86%、99.54%和97.81%。最后,本文通過基于MPS450多參數(shù)模擬儀組成的心電信號采集實驗系統(tǒng)對六類心電信號進行采集,并對其進行特征提取和分類算法驗證,識別準確率達到了 99.33%,平均靈敏度、平均特異度和平均陽性預測值分別為99.33%、99.87%和 99.36%。實驗結(jié)果表明本文提出的特征提取算法和分類算法能夠有效地提取穩(wěn)定的心電信號特征,并通過遺傳算法優(yōu)化的神經(jīng)網(wǎng)絡分類器實現(xiàn)了對六類心電信號的高精度分類。因此,本文提出的心電信號分類方法可以有效地用于心律失常識別,對于心臟病的預防、診斷和治療具有重要的意義。
[Abstract]:Electrocardiogram (ECG) is the most direct reflection of human heart electrical activity and one of the important bases for doctors to diagnose and treat heart disease. With the development of science and technology, Automatic analysis and diagnosis technology based on electrocardiogram has been widely used in heart disease detection and diagnosis. The automatic analysis and diagnosis technology based on electrocardiogram not only greatly reduces the workload of doctors, Moreover, it can improve the efficiency and accuracy of ECG classification, and has important practical value for the timely diagnosis and treatment of heart disease. In this paper, the classification and recognition method of ECG signal in ECG automatic analysis and diagnosis technology is studied deeply. The main research contents include the feature extraction and feature classification of ECG signals. The extraction of stable and effective ECG features is an important part of ECG automatic analysis and diagnosis technology. In this paper, a new ECG feature extraction algorithm based on wavelet packet decomposition and statistical analysis is proposed. Then the singular value, standard deviation and maximum value of 16 wavelet packet coefficients on the fourth scale after wavelet packet decomposition are calculated by using statistical analysis method. In order to improve the efficiency and accuracy of ECG classification and recognition, the statistical features of 48 dimensional wavelet packet coefficients are used to form the ECG feature space. In this paper, an algorithm of ECG feature selection and classification based on genetic algorithm optimization neural network is proposed. By using genetic algorithm to reduce the dimension of ECG feature space, 25 dimensional ECG feature can be obtained. At the same time, genetic algorithm is used to optimize the weights and thresholds of the neural network classifier with error back propagation, and the reduced dimension ECG features are input into the classifier for training and prediction. Thus, the classification of six kinds of ECG signals in MIT-BIH arrhythmia database: normal heartbeat, left bundle branch block, right bundle branch block, pacing heartbeat, ventricular premature beat and atrial premature beat was realized. The accuracy of recognition of the test set was 97.78 and the average sensitivity was 97.78. The average specificity and average positive predictive value are 97.86% and 97.81%, respectively. Finally, six kinds of ECG signals are collected by an experimental system of ECG signal acquisition based on MPS450 multiparameter analog instrument, and their feature extraction and classification algorithm are verified. The recognition accuracy is 99.33, the average sensitivity, average specificity and average positive predictive value are 99.33% and 99.36%, respectively. The experimental results show that the proposed feature extraction algorithm and classification algorithm can effectively extract stable ECG features. The neural network classifier optimized by genetic algorithm is used to realize the high accuracy classification of six kinds of ECG signals. Therefore, the ECG classification method proposed in this paper can be used to identify arrhythmia effectively and prevent heart disease. Diagnosis and treatment are of great significance.
【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】：R540.4;TP18

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