Quantitative evaluation of signal integrity for magnetocardiography

Abstract

Magnetocardiography (MCG) is a non-invasive diagnostic tool used to investigate the activity of the heart. For applications in an unshielded environment, in order to extract the very weak signal of interest from the much higher background noise, dedicated hardware configuration and sophisticated signal processing techniques have been developed during the last decades. Being powerful in noise rejection, the signal processing may introduce signal distortions, if not properly designed and applied. However, there is a lack of an effective tool to quantitatively evaluate the signal integrity for MCG at present. In this paper, we have introduced a very simple method by using a small coil driven by a human ECG signal to generate a simulated MCG signal. Three key performance indexes were proposed, which are correlation in time domain, relative heights of different peaks and correlation in frequency domain, to evaluate the MCG system performance quantitatively. This evaluation method was applied to a synthetic gradiometer consisting of a second-order axial gradiometer and three orthogonal reference magnetometers. The evaluation turned out to be very effective in optimizing the parameters for signal processing. In addition, the method can serve as a useful tool for hardware improvement.

Baseline optimization of SQUID gradiometer for magnetocardiography

“…The k 1 , k 2 , and k 3 are the synthesis coefficients, which are obtained by the least-square algorithm. [9] Considering the fact that the directions of M 0 and M i cannot be configured to be absolutely parallel to each other, two XY SQUID magnetometer references are added to compensate for the gradiometer imbalance. We introduce the SNR as an index to optimize the gradiometer baseline.…”

Multichannel fetal magnetocardiography using SQUID bootstrap circuit

“…High frequency noise, low frequency drifts, maternal MCG, and other artifacts were removed by a combination of digital low-pass FIR filter with a cutoff frequency of 90 Hz, reference compensation with a smoothing least-square (LS) algorithm and fixedpoint independent component analysis (FastICA) algorithm respectively. [12][13][14][15][16] Afterwards, the fetal MCG data were averaged to obtain a higher signal-to-noise ratio (SNR).…”

Model design of a superconducting quantum interference device of magnetic field sensors for magnetocardiography

“…The ECG and MCG statistical information are complementary, but, overall, the human mind is responsible for accurate diagnosis. When both ECG and MCG data are used in analysis and diagnosis, errors in the diagnosis of cardiac pathologies can be reduced by half, compared to using ECG only [9][10][11][12]. Additionally, the effectiveness of MCG has been validated in patients with various cardiac abnormalities such as ischemia, cardiomyopathies, atrial and ventricular arrhythmias, etc.…”