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Magnetocardiogram (MCG) measurement systems require noise reduction, because MCG signals are extremely small compared to environmental magnetic noise. We investigate the efficacy of a novel noise-reduction method, based on an independent component analysis (ICA). The proposed noise reduction method requires a component selection process to distinguish signal from noise. A major challenge in applying ICA-based noise reduction method is the selection of suitable parameters, which in practice is often performed manually with rather subjective parameter choices. To address this issue, we proposed a component selection method that can be performed quantitatively and automatically. The proposed method is based on the peak values of the autocorrelation function and helps distinguish the independent components of the MCG signals from the noise using an appropriate threshold. By using the proposed method, we obtain output signal-to-noise ratios (SNRs) of 33.98 dB, 19.17 dB, and 13.56 dB, corresponding to input SNRs for the simulated data at respectively 0 dB, -10 dB, and -20 dB, after noise reduction. The results show that the proposed method exhibits remarkable promise in extracting a noise-mitigated MCG signal for a wide range of SNRs.

Automatic component selection for noise reduction in magnetocardiograph based on independent component analysis