Magnetocardiography(MCG)has attracted considerable attention in the field of heart disease prevention and diagnosis,attributed to its non-invasive,contact-free,and high-precision characteristics.However,MCG signals are extremely weak,making denoising processing imperative for subsequent analysis.Herein,a multi-level cooperative denoising algorithm tailored to the noise characteristics of MCG signals is proposed.This algorithm linearly integrates empirical mode decomposition,variational mode decomposition,and complete ensemble empirical mode decomposition with adaptive noise.Specifically,empirical mode decomposition is firstly employed to eliminate baseline drift.Subsequently,hunter interferes with sparrow search algorithm is utilized to optimize the parameters of variational mode decomposition,and components carrying the principal features are filtered out using the correlation coefficient as the threshold.Finally,complete ensemble empirical mode decomposition with adaptive noise is incorporated to accurately remove Gaussian white noise from the signals.Experimental comparisons demonstrate that the proposed algorithm can preserve the principal features of MCG signals while maximizing the filtration of environmental noise,achieving an average base variance of 1.4927,a maximum base variance of 1.649 4,an average signal-to-noise ratio of 24.267 7,and a minimum signal-to-noise ratio of 22.867 7,outperforming traditional algorithms,and exhibiting the excellent noise filtering performance.

Magnetocardiography(MCG)has attracted considerable attention in the field of heart disease prevention and diagnosis,attributed to its non-invasive,contact-free,and high-precision characteristics.However,MCG signals are extremely weak,making denoising processing imperative for subsequent analysis.Herein,a multi-level cooperative denoising algorithm tailored to the noise characteristics of MCG signals is proposed.This algorithm linearly integrates empirical mode decomposition,variational mode decomposition,and complete ensemble empirical mode decomposition with adaptive noise.Specifically,empirical mode decomposition is firstly employed to eliminate baseline drift.Subsequently,hunter interferes with sparrow search algorithm is utilized to optimize the parameters of variational mode decomposition,and components carrying the principal features are filtered out using the correlation coefficient as the threshold.Finally,complete ensemble empirical mode decomposition with adaptive noise is incorporated to accurately remove Gaussian white noise from the signals.Experimental comparisons demonstrate that the proposed algorithm can preserve the principal features of MCG signals while maximizing the filtration of environmental noise,achieving an average base variance of 1.4927,a maximum base variance of 1.649 4,an average signal-to-noise ratio of 24.267 7,and a minimum signal-to-noise ratio of 22.867 7,outperforming traditional algorithms,and exhibiting the excellent noise filtering performance.