Research On Denoising And Inversion Technique Of Magnetocardiography Signal

The heart is one of the most important organs of the human body,so heart disease is a serious threat to human health.Obtaining cardiac electrical function information by measuring the magnetic field strength of the human body is a new technique for detecting heart disease.The superconducting quantum interference devices is used internationally to detect the magnetocardiography(MCG)signal.However,the measurement signal is seriously affected by noise,and the signal characteristics are submerged in the background noise.Therefore,the MCG signal filtering technique is performed to suppress noise.In order to further obtain the parameters related to cardiac electrical function,it is necessary to use magnetic resonance inversion technique to reconstruct the current source of the denoised multi-channel signals.Reconstructing the current source provides important information for the diagnosis of heart disease.Therefore,it is of great theoretical and practical value to study the denoising algorithms of the MCG signal and the inversion algorithms of reconstructing current source.In this paper,a variety of filtering algorithms are studied by using signal decomposability and quasi-periodicity.Based on the cardiac magnetic field model,the reconstruction inversion algorithms are studied.The main work of the paper is as follows:1.Analyze the type of noise existing in the MCG signal and the influence on the signal.Based on the characteristics of signal and noise,propose the filtering problem of the data of cardiac electromagnetic field detection.To solve this problem,some algorithms are studied,including: wavelet threshold denoising algorithm,empirical mode decomposition algorithm and fast independent component analysis algorithm to denoise the MCG signal.In addition,the detailed denoising steps of the algorithms are given.The simulation results show that the signal-to-noise ratio improved by classical filtering algorithms is limited.2.Based on signal decomposition theory,research the ensemble empirical mode decomposition algorithm and the variational mode decomposition algorithm.The simulation results show that the two algorithms can avoid the problems of the classical algorithms,but the output signal-to-noise ratio still needs to be improved.For the shortcomings of variational mode decomposition,the correlation coefficient variable is introduced to remove the baseline drift noise,and a new iterative formula is proposed for signal decomposition.The simulation analysis shows that the improved algorithm improves the signal-to-noise ratio more and the denoising effect is better.3.Based on the neighborhood correlation of the signal,the periodic non-local mean filtering algorithm and the total variation denoising algorithm are introduced into the denoising of the MCG signal.The simulation results show that the two algorithms can retain more signal features,but there are still deviations in the denoising of the spike band.For this problem,an improved total variation denoising algorithm is proposed.The adaptive constraint parameters are defined.The first derivative matrix in the original canonical term is changed to a new second-order matrix.The simulation performance analysis shows that the improved algorithm can effectively improve the signal-to-noise ratio.The mean square error is better than other algorithms,and the performance of the algorithm is better.4.On the basis of the magnetic dipole model of the heart,the cardiac function model is established.The distribution of the cardiac current source is estimated by inverting the denoised magnetic field array signal,and the estimation results are analyzed.The simulation results show that the goodness of fit between the original magnetic field and the magnetic field generated by the reconstructed current source is good.