The detection of the basic electric rhythm (BER), composed of a 3 cycles min(-1) oscillation, can be performed using SQUID magnetometers. However, the electric response activity (ERA), which is generated when the stomach is performing a mechanical activity, was detected mainly by invasive electrical measurements and only recently was one report published describing its detection by magnetic measurements. This study was performed with the aim of detecting the ERA noninvasively after a meal. MGG recordings were made with a 74-channel first-order gradiometer (Magnes II, biomagnetic technologies) housed in a shielded room. Seven nonsymptomatic volunteers were measured in the study. Initially a 10 min recording was performed with the subject in the fasted state. A 250 kcal meal was given to the subject without moving out of the magnetometers and two epochs of 10 min each were acquired. The signals were processed to remove cardiac interference by an algorithm based on a variation of independent component analysis (ICA), then autoregressive and wavelet analysis was performed. Preliminary results have shown that there is an increase in the signal power at higher frequencies around (0.6 Hz-1.3 Hz) usually associated with the basic electric rhythm. The center of the frequency band and its width varied from subject to subject, demonstrating the importance of pre-prandial acquisition as a control. Another interesting finding was an increase in power after about 5 min of meal ingestion. This period roughly agrees with the lag phase of gastric emptying, measured by scintigraphy and other techniques. We confirm that MGG can detect the electric response activity in normal volunteers. Further improvements in signal processing and standardization of signal acquisition are necessary to ascertain its possible use in clinical situations.

No abstract

Fetal magnetocardiography (fMCG) has been extensively reported in the literature as a non-invasive, prenatal technique that can be used to monitor various functions of the fetal heart. However, fMCG signals often have low signal-to-noise ratio (SNR) and are contaminated by strong interference from the mother's magnetocardiogram signal. A promising, efficient tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). Herein we propose an algorithm based on a variation of ICA, where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We model the system using autoregression, and identify the signal component of interest from the poles of the autocorrelation function. We show that the method is effective in removing the maternal signal, and is computationally efficient. We also compare our results to more established ICA methods, such as FastICA.

Functional MRI (fMRI) data often have low signal-to-noise-ratio (SNR) and are contaminated by strong interference from other physiological sources. A promising tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). BSS is based on the assumption that the detected signals are a mixture of a number of independent source signals that are linearly combined via an unknown mixing matrix. BSS seeks to determine the mixing matrix to recover the source signals based on principles of statistical independence. In most cases, extraction of all sources is unnecessary; instead, a priori information can be applied to extract only the signal of interest. Herein we propose an algorithm based on a variation of ICA, called Dependent Component Analysis (DCA), where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We applied such method to inspect functional Magnetic Resonance Imaging (fMRI) data, aiming to find the hemodynamic response that follows neuronal activation from an auditory stimulation, in human subjects. The method localized a significant signal modulation in cortical regions corresponding to the primary auditory cortex. The results obtained by DCA were also compared to those of the General Linear Model (GLM), which is the most widely used method to analyze fMRI datasets.

No abstract