本文選題：心電信號 + 心電信號建模��； 參考：《蘭州理工大學(xué)》2017年碩士論文

【摘要】：心臟性猝死是一種對人類生命有巨大威脅的疾病,大多數(shù)學(xué)者將猝死的時間限定在發(fā)病1小時之內(nèi),如果能在發(fā)病前對心臟性猝死疾病進行預(yù)警,就可以在心臟性猝死發(fā)生前挽救患者的生命。心電信號是心臟機械收縮與舒張的反映,是非平穩(wěn)信號,但卻表現(xiàn)出一定的準周期特性,即心電信號具有循環(huán)平穩(wěn)特性,當(dāng)人體內(nèi)發(fā)生心臟性猝死疾病時,自身的循環(huán)平穩(wěn)特性也會發(fā)生改變。傳統(tǒng)的心血管疾病識別是把心電信號作為平穩(wěn)信號進行分析,但心電信號是非平穩(wěn)的,因此采用循環(huán)平穩(wěn)算法對心電信號進行特征提取,結(jié)合支持向量機對心臟性猝死進行識別。論文主要工作如下:(1)心電信號濾波是特征提取的前提,針對心電信號采集過程中的常見噪聲,采用適合非平穩(wěn)信號處理的小波變換算法對心電信號進行濾波。首先對心電信號進行建模仿真,產(chǎn)生干凈的心電信號,通過添加不同信噪比的噪聲來評價濾波器效果。其次設(shè)計了小波變換濾波器,通過和整系數(shù)濾波器相比較,本文設(shè)計的小波變換濾波器對心電信號的濾波效果更好。最后用實際心電信號對小波變換濾波器進行驗證,實驗結(jié)果表明:小波變換濾波能夠有效地去除高頻和低頻噪聲。(2)根據(jù)心電信號表現(xiàn)出的循環(huán)平穩(wěn)特性,首先介紹了循環(huán)平穩(wěn)的基本概念一階和二階循環(huán)平穩(wěn),并由二階循環(huán)平穩(wěn)推導(dǎo)了反映心電信號循環(huán)平穩(wěn)特性的積分循環(huán)功率譜密度函數(shù);接著詳細分析了實時性較高的時域平滑循環(huán)譜估計算法——FFT累加算法,并對正弦信號進行了循環(huán)譜的理論計算和仿真估計,實驗結(jié)果表明:循環(huán)譜仿真估計結(jié)果與理論計算結(jié)果一致;最后在此基礎(chǔ)上,提取不同典型人群心電信號的循環(huán)平穩(wěn)特征,并利用心電信號的循環(huán)平穩(wěn)特性對干擾段進行了檢測。(3)針對心臟性猝死識別準確率不高的問題,提出了基于心電信號循環(huán)平穩(wěn)特征的識別方法。在循環(huán)平穩(wěn)理論的基礎(chǔ)上,提取了不同典型人群心電信號的循環(huán)平穩(wěn)特征,結(jié)合支持向量機對心臟性猝死進行識別,實驗得出循環(huán)頻率均值最能反映循環(huán)平穩(wěn)特征,比較了兩類線性分類器與支持向量機的識別效果,最后采用支持向量機和現(xiàn)有心臟性猝死識別方法進行對比,實驗結(jié)果表明基于心電信號循環(huán)平穩(wěn)特征的心臟性猝死識別方法在準確性上有明顯的優(yōu)勢,猝死心電信號的識別準確率最高可達97.50%。
[Abstract]:Sudden cardiac death is a disease with great threat to human life. Most scholars limit the time of sudden death to one hour. The patient's life can be saved before sudden cardiac death occurs. ECG signal is the reflection of cardiac mechanical contraction and relaxation, which is non-stationary signal, but it shows certain quasi-periodic characteristic, that is, electrocardiogram signal has the characteristic of circulatory stability, when sudden cardiac death occurs in human body, Its own cycle stability will also change. In traditional cardiovascular disease recognition, ECG signals are analyzed as stationary signals, but ECG signals are non-stationary. Therefore, a cyclic stationary algorithm is used to extract the features of ECG signals. To identify sudden cardiac death with support vector machine (SVM). The main work of this paper is as follows: (1) ECG filtering is the premise of feature extraction. Aiming at the common noise in ECG signal acquisition, wavelet transform algorithm suitable for non-stationary signal processing is used to filter ECG signal. Firstly, the ECG signal is modeled and simulated to generate clean ECG signal, and the filter effect is evaluated by adding different SNR noise. Secondly, the wavelet transform filter is designed. Compared with the integer coefficient filter, the wavelet transform filter designed in this paper has better effect on ECG signal filtering. Finally, the actual ECG signal is used to verify the wavelet transform filter. The experimental results show that the wavelet transform filter can effectively remove the high frequency and low frequency noise. Firstly, the basic concepts of cyclic stationarity, first and second order, are introduced, and the integral cyclic power spectral density function, which reflects the cyclic stationary characteristic of ECG signal, is derived from the second-order cyclic stationarity. Then, the time-domain smoothing cyclic spectrum estimation algorithm, FFT accumulative algorithm, is analyzed in detail, and the theoretical calculation and simulation of the cyclic spectrum of sinusoidal signal are carried out. The experimental results show that the simulation results of cyclic spectrum are consistent with the theoretical results. Finally, the cyclic stationary characteristics of ECG signals of different typical populations are extracted. In order to solve the problem that the accuracy of sudden cardiac death recognition is not high, a method based on cyclic stationary characteristic of ECG signal is proposed to detect the disturbance segment. On the basis of the theory of cyclic stationary, the cyclic stationary characteristics of ECG signals of different typical people are extracted, and the sudden cardiac death is identified by using support vector machine. The experimental results show that the mean value of cycle frequency can best reflect the characteristics of circulatory stability. The recognition effects of two kinds of linear classifiers and support vector machines (SVM) are compared. Finally, support vector machines (SVM) and existing methods of sudden cardiac death (SCD) recognition are compared. The experimental results show that the recognition method of sudden cardiac death based on the steady characteristics of ECG cycle has obvious advantages in accuracy, and the recognition accuracy of sudden death signal can reach 97.50%.
【學(xué)位授予單位】：蘭州理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：R541.78;TN911.7

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