本文選題：心電信號 + 小波變換　； 參考：《吉林大學(xué)》2014年碩士論文

【摘要】：心臟病突發(fā)性強(qiáng)但病情隱蔽，死亡率一直居高不下，已然成為世界性的重大公共衛(wèi)生問題。醫(yī)學(xué)中往往通過心電圖對心率失常進(jìn)行分析診斷，因此心電信號自動分析診斷系統(tǒng)的研究開發(fā)具有很高的臨床價值。由于心電信號是一種極易受環(huán)境影響的非線性的微弱信號，而且常夾雜各種噪聲干擾，增加了分析診斷的難度，因此提高心電自動分析檢測系統(tǒng)的精度和準(zhǔn)確性，，是心血管疾病的診斷的前提。 本文主要研究了心電信號的去噪和特征波檢測兩方面的關(guān)鍵技術(shù)，利用FPGA器件處理信號時所具有的實時性、可靠性、高效性、可并行處理等優(yōu)勢，加快了運算處理的速度，并在Matlab軟件分析的基礎(chǔ)上設(shè)計了基于小波變換的從心電信號預(yù)處理到R波檢測的一整套檢測系統(tǒng)。 預(yù)處理算法：本文基于db4提升小波變換提出了一種面向FPGA硬件的去噪方案，首先選擇了合適的小波基和分解層數(shù)，對提升小波變換后各層上的小波系數(shù)采用軟閾值的方法進(jìn)行處理，進(jìn)一步基于Cyclone II系列EP2C35F672C8核心芯片，利用MATLAB/DSP Builder、Quartus II等軟件以及VHDL語言設(shè)計了該算法的SOPC硬件電路，對系統(tǒng)中各個模塊進(jìn)行功能仿真，并面向FPGA硬件實現(xiàn)。驗證結(jié)果表明本算法能夠在保證原信號不失真的情況下，較好地去除基線漂移、工頻干擾、肌電干擾等噪聲。最后，通過對系統(tǒng)的實現(xiàn)時間和功耗進(jìn)行分析得出本文提出的系統(tǒng)具實時性和低功耗性。 特征波檢測算法：本檢測算法是在選擇雙正交樣條小波作為小波基的基礎(chǔ)上，根據(jù)Mallat算法和小波變換的奇異點檢測原理，以及心電信號在奇異點處的Lipschitz指數(shù)說明奇異點與模極值對的關(guān)系，進(jìn)而定位心電信號的R波，并提出了避免誤檢和漏檢的策略，有效地提高了檢測準(zhǔn)確率。其次在小波變換的基礎(chǔ)上設(shè)計了面向硬件FPGA實現(xiàn)的心電信號R波檢測系統(tǒng)。檢測系統(tǒng)的硬件實現(xiàn)主要分兩步完成：首先對去噪后的心電信號進(jìn)行小波變換，其實對小波變換的輸出結(jié)果進(jìn)行檢測。本系統(tǒng)采用流水線操作實現(xiàn)4級的小波變換，即采用級聯(lián)的方式連接每一層的小波變換，提高了數(shù)據(jù)運算效率，其中各層小波變換可作為基本運算單元。把小波變換第三、四尺度上的輸出結(jié)果進(jìn)行檢測，根據(jù)檢測策略在這兩個尺度上尋找模極值對的過零點，即可定位R波。此外，本文還利用48組心電數(shù)據(jù)對該檢測算法進(jìn)行波形仿真驗證，實驗結(jié)果表明本文提出的R波檢測算法具有較高的檢測率和穩(wěn)定性，有一定的實用價值。
[Abstract]:Heart disease has become a major public health problem in the world. Electrocardiogram (ECG) is often used to analyze and diagnose arrhythmias in medicine, so the research and development of ECG automatic analysis and diagnosis system has high clinical value. The ECG signal is a kind of nonlinear weak signal easily affected by the environment, and is often mixed with various kinds of noise interference, which makes the analysis and diagnosis more difficult, so the accuracy and accuracy of the ECG automatic analysis and detection system are improved. Is a prerequisite for the diagnosis of cardiovascular disease. In this paper, the key technologies of ECG signal denoising and characteristic wave detection are studied. The advantages of real-time, reliability, high efficiency and parallel processing of FPGA devices are used to accelerate the processing speed. Based on the analysis of Matlab software, a set of detection system from ECG signal preprocessing to R wave detection based on wavelet transform is designed. Preprocessing algorithm: based on db4 lifting wavelet transform, this paper proposes a denoising scheme for FPGA hardware. Firstly, the appropriate wavelet basis and decomposition layer number are selected. The wavelet coefficients on each layer after lifting wavelet transform are processed by the method of soft threshold. Further, based on Cyclone II series EP2C35F672C8 core chips, the SOPC hardware circuit of the algorithm is designed by using MATLAB/DSP Builder Quartus II and VHDL language. The function of each module in the system is simulated, and the FPGA hardware is implemented. The results show that the proposed algorithm can remove baseline drift, power frequency interference and electromyography interference without distortion of the original signal. Finally, by analyzing the implementation time and power consumption of the system, it is concluded that the proposed system is real-time and low power consumption. Characteristic wave detection algorithm: based on the selection of biorthogonal spline wavelet as wavelet basis, the detection algorithm is based on Mallat algorithm and singular point detection principle of wavelet transform. The Lipschitz exponents of ECG signals at singularity points illustrate the relationship between singularity points and mode-extremum pairs, and then locate R waves of ECG signals. The strategies of avoiding false detection and missing detection are put forward, which can effectively improve the accuracy of detection. Secondly, the R wave detection system of ECG signal based on wavelet transform is designed for hardware FPGA. The hardware implementation of the detection system is mainly divided into two steps: firstly, wavelet transform is applied to the de-noised ECG signal, but in fact, the output result of the wavelet transform is detected. The system adopts pipeline operation to realize the four-level wavelet transform, that is, the wavelet transform of each layer is connected in cascade mode, which improves the efficiency of data operation, and the wavelet transform of each layer can be used as the basic operation unit. The output results of the third and fourth scales of wavelet transform are detected. According to the detection strategy, the zero-crossing points of the mode-extremum pair are found on these two scales, and the R-wave can be located. In addition, 48 sets of ECG data are used to verify the waveform of the algorithm. The experimental results show that the proposed R-wave detection algorithm has a higher detection rate and stability, and has a certain practical value.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN911.6

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