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In the routine recording of magnetocardiograms (MCGs), it is necessary to underline the problem of noise cancellation. Source separation has often been suggested to solve this problem. In this paper, blind source separation (BSS), by means of singular value decomposition (SVD) and independent component analysis (ICA), was used for noise reduction in MCG data to improve the signal to noise ratio. Special techniques, based on statistical parameters, for identifying noise and disturbances, have been introduced to automatically eliminate noise-related and disturbance-related components before reconstructing cleaned data sets. The results show that ICA and SVD can detect and remove a variety of noise and artefact sources from MCG data, as well as from stress MCG.

Abstract: In 1992, Brockmeier et al. showed the difference in magnetocardiography (MCG)-detected field distribution generated by the heart at rest and under stress. Unfortunately, routinely recorded magnetocardiograms (MCGs) are corrupted by different types of artifacts, especially during stress. This work addressed the problem of optimizing artifacts removing and improving the signal to noise ratio (SNR) in patients with coronary heart disease (CHD) under pharmacological stress MCG (PSMCG) using a technique of Blind Source Separation (BSS) called TDSEP. Techniques based on simple statistical parameters were proposed to identify the cardiac related component among the unmixed data. The presented results showed that BSS could detect and remove a variety of noise and artifact sources in PSMCG, allowing the use in clinical applications. [Copyright &y& Elsevier]