Considering the properties of slow change and quasi-periodicity of magnetocardiography （MCG） signal, we use an integrated technique of adaptive and low-pass filtering in dealing with two-channel MCG data measured by high Tc SQUIDs. The adaptive filter in the time domain is based on a noise feedback normalized least-mean-square （NLMS） algorithm, and the low-pass filter with a cutoff at 100Hz in the frequeucy domain characterized by Caussian functions is combined with a notch at the power line frequency. In this way, both relevant and irrelevant noises in original MCG data are largely eliminated. The method may also be useful for other slowly varying quasi-periodical signals.
In this paper, we have developed an algorithm based on singular value decomposition （SVD） for matrix. And the novel SVD algorithm with normalized period of cardiac cycles is presented. The results from real magnetocardiography （MCG） data processing show that the new algorithm is better than the standard one not only in suppressing noises, but also in providing high-fidelity MCG signals.
We present a new least-mean-square algorithm of adaptive filtering to improve the signal to noise ratio for magneto-cardiography data collected with high-temperature SQUID-based magnetometers. By frequently adjusting the adaptive parameter a go systematic optimum values in the course of the programmed procedure, the convergence is accelerated with a highest speed and the minimum steady-state error is obtained simultaneously. This algorithm may be applied to eliminate other non-steady relevant noises as well.
