Research On The Method And Application Of Adaptive Denoising Of ECG Signals

With the development and progress of society,people pay more and more attention to their health.However,due to work pressure,unreasonable diet and genetic factors,a large number of people are susceptible to heart diseases such as high blood pressure and coronary heart disease when they reach a certain age,which has a great impact on people's health.harm.Electrocardiogram,as one of the most accurate means of monitoring human cardiac electrical activity,is an effective basis for medical staff to diagnose patients with cardiovascular diseases.However,the ECG signal is a very weak signal,and it will be subject to various interferences during the acquisition process,which brings difficulties for the doctor to judge the patient's pathological information.Therefore,the removal of ECG signal noise has become an important research topic.In view of the defects of traditional ECG signal denoising algorithm,such as large error,low denoising efficiency,and missing waveform after denoising,this paper focuses on improving the denoising efficiency of the algorithm and obtaining a complete ECG waveform after denoising.Two adaptive algorithms,and obtained good results through simulation.First of all,the thesis aims at the defects of Ensemble Empirical Mode Decomposition(EEMD)in the denoising of ECG signals,which is poor in completeness and too much calculation,and proposes the application of improved adaptive CEEMD method in denoising ECG signals.Based on the analysis of the noise addition criterion,this method introduces Peak Error(PE)as an evaluation index for adding noise,and uses the peak error to adaptively determine the optimal noise addition amplitude;then the original signal and the amplitude of the added noise are used The ratio coefficient of the standard deviation adaptively obtains the total average number of decompositions for different signals;finally,the method is applied to the MIT-BIH ECG database established by the Massachusetts Institute of Technology in the United States,and the target signal is well achieved noise.Experiments show that the average signal to noise ratio(SNR)of the proposed method reaches 19.2497,the minimum mean square error(Root Mean Square Error,RMSE)is only 0.0473,and the average smoothness index R is only 0.0305.The algorithm effectively removes the original ECG signal noise,improves the smoothness of the signal,and improves the operation efficiency.Secondly,for the difficulty of normalizing the Least Mean Square(NLMS)algorithm in ECG signal denoising,it is difficult to balance the calculation efficiency and steady-state error.In the normalized least mean square algorithm and the minimum mean of momentum Based on the research of Momentum Least Mean Square(MLMS),an improved adaptive NLMS method is proposed to denoise ECG signals.This method first performs LMS filtering on the ECG signal,then adaptively determines the size of the variable step size through the relative error before and after the ECG signal filtering,and finally sets the expected error to determine whether the denoising meets the expected requirements,and does not meet the requirements again.The first step of LMS filtering is to output the denoised ECG signal after meeting the requirements.Then the method is applied to the MIT-BIH ECG database established by the Massachusetts Institute of Technology,and the target signal is well denoised.Experiments show that the average SNR of the proposed method is 17.6016,the RMSE is only 0.0334,and the average smoothness index R reaches 0.0325.In terms of subjective evaluation,a comparative analysis of the waveforms of several algorithms before and after denoising proves that the algorithm has a better denoising effect.Finally,design an adaptive ECG signal denoising system,including ECG signal acquisition module,ECG signal processing module,MCU control module and display module,and then apply the proposed algorithm to the system.The design first preprocesses the collected ECG signals with various filters;in order to obtain a purer ECG signal,the preprocessed ECG signal is combined with an improved adaptive CEEMD ECG signal denoising algorithm,De-noise the ECG signal again;finally display the de-noised ECG signal on the smart terminal for easy monitoring and diagnosis.