Automatic Identification and Suppression of Metal Artifacts in Multichannel OPM-MCG Data Based on Second-Order Blind Identification Method

Magnetocardiography (MCG) plays a growing role in noninvasive cardiac disease diagnosis. However, MCG signals are prone to environmental magnetic fields and metal artifacts, distorting waveforms and affecting diagnostic accuracy. Existing methods like the fast independent component analysis (FastICA) and information maximization (Infomax) algorithm have limitations in suppressing ultralow frequency metal artifacts. We propose a second-order blind identification (SOBI) algorithm based on an optimized time-delay matrix, utilizing temporal coherence to effectively separate ultralow frequency metal artifacts from mixed sources. An automatic screening method for metal artifacts, QRS, T/P waves, and unknown interferences is established using time-frequency features. Extensive simulations and real OPM-MCG experiments validate our method’s superiority in metal artifact suppression. The results show that our method surpasses FastICA and Infomax in suppressing metal artifacts, achieving average SNR improvement of 5.36%–29.40% across four subjects. Reconstructed P/QRS/T waves are undistorted, with a minimum 80.71% reduction in RMSE. This method potentially expands MCG’s clinical applications, benefiting more patients.