心電信號(hào)自動(dòng)分析技術(shù)研究
發(fā)布時(shí)間：2018-10-23 20:42
【摘要】：心電圖一直是人們了解自身心臟特征的主要途徑，是疾病診斷的重要依據(jù)。由于動(dòng)態(tài)心電圖的產(chǎn)生，導(dǎo)致手工分析心電圖所有數(shù)據(jù)已經(jīng)不可能，為了提高診斷效率，實(shí)時(shí)監(jiān)測病人，心電信號(hào)自動(dòng)分析技術(shù)的誕生是必然的。 心電信號(hào)屬于微弱信號(hào)，采集信號(hào)一般都包含各種干擾噪聲。因此，首先要對(duì)信號(hào)進(jìn)行去噪處理，心電去噪是QRS波形檢測和特征提取的基礎(chǔ)，其結(jié)果將直接影響到自動(dòng)分析的診斷結(jié)果。QRS波是心電圖中最明顯的部位，包含了很多重要生理信息，因此，QRS檢測是自動(dòng)分析中重要的一步，不僅是其他波形定位的基礎(chǔ)，也是特征提取的前提，將影響自動(dòng)分析診斷的準(zhǔn)確度。本文基于前人研究成果的基礎(chǔ)上，主要對(duì)QRS波群檢測技術(shù)做了進(jìn)一步研究。 在去噪處理方面，本文采用了小波閾值去噪法。主要工作：1.選擇合適的小波函數(shù)并確定小波分解層數(shù)。2.選擇合適的閾值函數(shù)和閾值估計(jì)方法。3.進(jìn)行仿真實(shí)驗(yàn)。本文利用sym8小波對(duì)心電進(jìn)行小波分解，采用小波硬閾值法處理信號(hào)，同時(shí)通過輸出信噪比(SNR)和最小均方誤差(MSE)兩參數(shù)對(duì)實(shí)驗(yàn)結(jié)果進(jìn)行評(píng)估，表明該方法能夠有效的去除心電信號(hào)中的主要噪聲，具有很好的去噪效果。 本文提出了一種基于小波變換的R峰定位方法。該方法采用了高斯小波作為小波函數(shù)，選取了能量集中、噪聲等干擾較弱的第3層的小波分解系數(shù)作為研究對(duì)象。主要工作：1.初始閾值并確定自動(dòng)閾值變換規(guī)則。2.尋找在符合閾值條件的極值點(diǎn)，并通過一定方法以及優(yōu)化策略對(duì)極值點(diǎn)進(jìn)行正確配對(duì)。3.根據(jù)極值對(duì)確定R波峰在原信號(hào)的位置區(qū)間，并在該區(qū)間中找出最值，該值位置即為R波峰位置。4.通過不應(yīng)期生理原理對(duì)R定位結(jié)果進(jìn)行誤檢。5.仿真實(shí)驗(yàn)。本文對(duì)MIT-BIH數(shù)據(jù)庫中部分典型波形數(shù)據(jù)進(jìn)行了實(shí)驗(yàn)，實(shí)驗(yàn)結(jié)果表明該算法定位R波峰的正確率很高，是一個(gè)有效性算法。 以定位R波峰為基礎(chǔ)，本文實(shí)現(xiàn)了QRS波群寬度的提取。主要工作：1.確定Q、S波在R波旁邊的大概范圍，在此范圍中尋找極值點(diǎn)，，并對(duì)這些極值點(diǎn)進(jìn)行正確配對(duì)2.根據(jù)極值對(duì)確定R波峰在原信號(hào)的位置區(qū)間，并在該區(qū)間中找出最值，該值位置即為波峰（Q或S波峰）。3.在Q波峰前或S波峰后8個(gè)采樣點(diǎn)中尋找斜率變化最大的采樣點(diǎn)，并認(rèn)為該點(diǎn)為Q波起始點(diǎn)或S波終點(diǎn)，即為QRS波群的始點(diǎn)和終點(diǎn)，算出QRS波群寬度5.仿真實(shí)驗(yàn)，在波形中標(biāo)出QRS波群寬度、Q波峰及S波峰。通過對(duì)MIT-BIH數(shù)據(jù)庫中部分典型波形數(shù)據(jù)進(jìn)行了實(shí)驗(yàn)，實(shí)驗(yàn)結(jié)果表明該方法有較好的精確度。
[Abstract]:Electrocardiogram (ECG) is the main way for people to understand their heart characteristics and an important basis for disease diagnosis. Because of the production of dynamic electrocardiogram, it is impossible to analyze all ECG data manually. In order to improve diagnosis efficiency and monitor patients in real time, the birth of ECG automatic analysis technology is inevitable. ECG signals belong to weak signals, and the collected signals generally contain all kinds of interference noise. Therefore, first of all, the signal should be de-noised. ECG denoising is the basis of QRS waveform detection and feature extraction, and the results will directly affect the diagnosis result of automatic analysis. QRS wave is the most obvious part of ECG. Therefore, QRS detection is an important step in automatic analysis, which is not only the basis of other waveform localization, but also the premise of feature extraction, which will affect the accuracy of automatic analysis and diagnosis. Based on the previous research results, this paper mainly focuses on the QRS wave group detection technology. In the aspect of denoising, wavelet threshold denoising method is adopted in this paper. Main work: 1. Select the appropriate wavelet function and determine the number of wavelet decomposition layers. 2. Select appropriate threshold function and threshold estimation method. 3. The simulation experiment is carried out. In this paper, sym8 wavelet is used to decompose ECG, and wavelet hard threshold method is used to process the signal. At the same time, the experimental results are evaluated by output SNR (SNR) and minimum mean square error (MSE). It shows that this method can effectively remove the main noise in ECG signal and has a good denoising effect. In this paper, a method of R peak location based on wavelet transform is proposed. In this method, Gao Si wavelet is used as the wavelet function, and the wavelet decomposition coefficient of the third layer, where the energy concentration and noise are weak, is chosen as the object of study. Main work: 1. Initial threshold and determine automatic threshold transform rules. 2. To find the extremum that meets the threshold condition, and make the correct pairing of the extremum by certain method and optimization strategy. 3. According to the extreme value pair, the position interval of R wave peak in the original signal is determined, and the maximum value is found in the interval. The position of the value is the position of the R wave peak. 4. The results of R localization were detected by the physiological principle of refractory period. 5. 5. Simulation experiment. In this paper, some typical waveform data in MIT-BIH database are experimented. The experimental results show that the algorithm has a high accuracy and is an effective algorithm. On the basis of locating R wave peak, the width of QRS wave group is extracted in this paper. Main work: 1. Determine the approximate range of QS waves next to R waves, search for extremum points in this range, and make the correct matching of these extreme points 2. According to the extreme value pair, the position interval of R wave peak in the original signal is determined, and the maximum value is found in the region. The position of the value is the peak (Q or S wave peak). 3. Among the 8 sampling points in front of Q wave peak or after S wave peak, the sampling point with the greatest slope change is found. It is considered as the starting point of Q wave or the end point of S wave, that is, the beginning and end point of QRS wave group, and the width of QRS wave group is calculated. Simulation experiments show that the width of QRS wave group, Q wave peak and S wave peak are marked in the waveform. Experiments on some typical waveform data in MIT-BIH database show that this method has good accuracy.
【學(xué)位授予單位】：南昌大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.6
本文編號(hào)：2290410
[Abstract]:Electrocardiogram (ECG) is the main way for people to understand their heart characteristics and an important basis for disease diagnosis. Because of the production of dynamic electrocardiogram, it is impossible to analyze all ECG data manually. In order to improve diagnosis efficiency and monitor patients in real time, the birth of ECG automatic analysis technology is inevitable. ECG signals belong to weak signals, and the collected signals generally contain all kinds of interference noise. Therefore, first of all, the signal should be de-noised. ECG denoising is the basis of QRS waveform detection and feature extraction, and the results will directly affect the diagnosis result of automatic analysis. QRS wave is the most obvious part of ECG. Therefore, QRS detection is an important step in automatic analysis, which is not only the basis of other waveform localization, but also the premise of feature extraction, which will affect the accuracy of automatic analysis and diagnosis. Based on the previous research results, this paper mainly focuses on the QRS wave group detection technology. In the aspect of denoising, wavelet threshold denoising method is adopted in this paper. Main work: 1. Select the appropriate wavelet function and determine the number of wavelet decomposition layers. 2. Select appropriate threshold function and threshold estimation method. 3. The simulation experiment is carried out. In this paper, sym8 wavelet is used to decompose ECG, and wavelet hard threshold method is used to process the signal. At the same time, the experimental results are evaluated by output SNR (SNR) and minimum mean square error (MSE). It shows that this method can effectively remove the main noise in ECG signal and has a good denoising effect. In this paper, a method of R peak location based on wavelet transform is proposed. In this method, Gao Si wavelet is used as the wavelet function, and the wavelet decomposition coefficient of the third layer, where the energy concentration and noise are weak, is chosen as the object of study. Main work: 1. Initial threshold and determine automatic threshold transform rules. 2. To find the extremum that meets the threshold condition, and make the correct pairing of the extremum by certain method and optimization strategy. 3. According to the extreme value pair, the position interval of R wave peak in the original signal is determined, and the maximum value is found in the interval. The position of the value is the position of the R wave peak. 4. The results of R localization were detected by the physiological principle of refractory period. 5. 5. Simulation experiment. In this paper, some typical waveform data in MIT-BIH database are experimented. The experimental results show that the algorithm has a high accuracy and is an effective algorithm. On the basis of locating R wave peak, the width of QRS wave group is extracted in this paper. Main work: 1. Determine the approximate range of QS waves next to R waves, search for extremum points in this range, and make the correct matching of these extreme points 2. According to the extreme value pair, the position interval of R wave peak in the original signal is determined, and the maximum value is found in the region. The position of the value is the peak (Q or S wave peak). 3. Among the 8 sampling points in front of Q wave peak or after S wave peak, the sampling point with the greatest slope change is found. It is considered as the starting point of Q wave or the end point of S wave, that is, the beginning and end point of QRS wave group, and the width of QRS wave group is calculated. Simulation experiments show that the width of QRS wave group, Q wave peak and S wave peak are marked in the waveform. Experiments on some typical waveform data in MIT-BIH database show that this method has good accuracy.
【學(xué)位授予單位】：南昌大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.6
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