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A method of postprocessing and visualizing three--dimensional vector fields, such as current density reconstruction results, is presented. This method is based on equivalent ellipsoids fitted to the vector fields. The technique has been tested with simulated data and current density reconstructions based on bioelectromagnetic data obtained from a physical thorax phantom. Three different approaches based on: 1) longest distance; 2) dominant direction; and 3) principal component analysis, for fitting the equivalent ellipsoids are proposed. Multiple foci in vector fields are extracted by multiple ellipsoids which are fitted iteratively. The method enables statistical postprocessing for the sake of comparisons of different source reconstructions algorithms or comparisons of groups of patients or volunteers. Index Terms--Biomagnetics, biomedical electromagnetic imaging, inverse problems, statistics, visualization.

Data from simulations of the anterior myocardial infarction (AMI) and inferior myocardial infarction (IMI) are presented. One infarct located in the anterior section of the left ventricle and a second one in the inferior wall of the left ventricle were modeled. A high-resolution finite element model of a heart and torso was used in this study. Differences in the normal and infarcted fields were computed. Our data suggest that the infarcted region contribution to the total magnetic field can be accounted for by an equivalent current dipole. It might also be possible to detect an infarct from these difference fields constructed for different cases of myocardial infarction. More simulations are needed to determine the relations between infarct sizes and locations and magnetic fields. These relations might then be used to detect various cases of myocardial infarction. Index Terms - Biomagnetic fields, biomagnetism, infarct simulations, magnetic fields of infarcts, MCG, torso models.

Magnetocardiograms (MCG's) simulated with a high-resolution heart-torso model of an adult subject were compared with measured MCG's acquired from the same individual. An exact match of the measured and simulated MCG's was not found due to the uncertainties in tissue conductivities and cardiac source positions. However, general features of the measured MCG's were reasonably represented by the simulated data for most, but not all of the channels. This suggests that the model accounts for the most important mechanisms underlying the genesis of MCG's and may be useful for cardiac magnetic field modeling under normal and diseased states. MCG's were simulated with a realistic finite-element heart-torso model constructed from segmented magnetic resonance images with 19 different tissue types identified. A finite-element model was developed from the segmented images. The model consists of 2.51 million brick-shaped elements and 2.58 million nodes, and has a voxel resolution of 1.56 x 1.56 x 3 mm. Current distributions inside the torso and the magnetic fields and MCG's at the gradiometer coil locations were computed. MCG's were measured with a Philips twin Dewar first-order gradiometer SQUID-system consisting of 31 channels in one tank and 19 channels in the other. Index Terms - Biomagnetic fields, biomagnetism, MCG, MCG simulations, torso fields, torso models.

Heart rate variability (HRV) is a marker of autonomous activity in the heart. An important application of HRV measures is the stratification of mortality risk after myocardial infarction. Our hypothesis is that the information entropy of HRV, a non-linear approach, is a suitable measure for this assessment. As a first step, to evaluate the effect of myocardial infarction on the entropy, we compared the entropy to standard HRV parameters. The entropy was estimated by compressing the tachogram with Bzip2. For univariate comparison, statistical tests were used. Multivariate analysis was carried out using automatically generated decision trees. The classification rate and the simplicity of the decision trees were the two evaluation criteria. The findings support our hypothesis. The meanNN-normalized entropy is reduced in patients with myocardial infarction with very high significance. One entropy parameter alone exceeds the discrimination strength of multivariate standards-based trees.

There is a lack of standard methods for the analysis of magnetocardiograms (MCGs). MCG signals have a shape similar to the ECG (P wave, QRS complex, T wave). High-quality multichannel recordings can indicate even slight disturbances of de- and repolarisation. The purpose of our study was to apply a new approach in the analysis of signal-averaged DC-MCGs. DC-MCGs (31-channel) were recorded in 182 subjects: 110 patients after myocardial infarction and 72 controls. Spatiotemporal correlation analysis of the QRS complex and T wave patterns throughout the entire heart cycle was used to analyse homogeneity of de- and repolarisation. These plots were compared to standard ECG analyses (electrical axis, Q wave, ST deviation, T polarity and shape). Spatiotemporal correlation analyses seem to be applicable in assessing the course of electrical repolarisation with respect to homogeneity. MCG provided all diagnostic information contained in common ECG recordings at high significance levels. The ECG patterns were included in 5/8 of our parameters for electrical axis, 6/8 for Q-wave, 7/8 for ST deviation and 5/8 for T-polarity based on two time series of correlation coefficients. We conclude that our spatiotemporal correlation approach provides a new tool for standardised analysis of cardiac mapping data such as MCG.