Arrhythmia vulnerability assessment using magnetic field maps and body surface potential maps.

Magnetic field maps and body surface potential maps can be used to measure cardiac activity. The ability of magnetic and potential body surface maps to identify patients' vulnerable to recurrent sustained ventricular arrhythmia (VA) were compared. Magnetic field maps (MFM) and body surface potential mapping (BSPM) were obtained from 76 normal (N) subjects, 15 myocardial infarct (MI) patients, and 15 VA patients. QRST integral maps were calculated for each subject and nondipolar content was determined using Karhunen-Loeve transform eigen-maps. Although differences in nondipolar content were significant between the normal and patient groups (P = 2.4 x 10(-5) for BSPM and P = 6.0 x 10(-8) for MFM), differences in nondipolar content between MI and VA patients using QRST integral BSPM and MFM maps were not significant. The trajectory of the location of the maxima and minima on the map area during the QRS and ST-T intervals were also constructed. Discrimination between MI and VA patients was based on intergroup differences in the amount of fragmentation of the trajectory plots. The ST-T trajectory plots were significantly more fragmented (P < 0.0001 and P < 0.05 for MFM and BSPM, respectively) for VA than for MI patients. The ST-T interval MFM and BSPM trajectory plots enabled separation of MI and VA patients with accuracies of 83% and 73%, respectively. These results suggest that repolarization MFM and BSPM extrema trajectory plots can be used effectively as a means of identifying patients at risk for VA.

Discrimination between myocardial infarct and ventricular tachycardia patients using magnetocardiographic trajectory plots and iso-integral maps.

Magnetocardiograms were recorded from 30 normal (N) subjects, 15 myocardial infarct (MI) patients, and 15 ventricular tachycardia (VT) patients. Discrimination between the groups was affected by iso-integral magnetic field mapping (MFM) and trajectory plotting of MFM extrema. Iso-integral MFM for the QRST, QRS, and ST-T intervals was created for each test group member. A polarity score, based on the number of extrema features present, was assigned to each iso-integral MFM. Differences in group mean integral of QRST map polarity scores were significant (p less than 0.05) between MI and N, between VT and N (p less than 0.005), and between MI and VT (p less than 0.05) subjects. integral of ST-T map polarity scores were significantly (p less than 0.0001) different between VT and N and between MI and VT (p less than 0.001) subjects. Discrimination between MI and VT patients, based on polarity score difference, was 56% accurate using integral of QRS maps and 73% accurate using integral of ST-T maps. For each subject, time-normalized MFM was used to construct trajectory plots of the maxima and minima in the QRS and ST-T intervals. Discrimination between MI and VT patients was based upon intergroup differences in fragmented trajectory plots. When the number of discrete trajectories and/or the total number (F) of trajectory points at which discrete trajectories coexist were considered, QRSmin trajectory plots were significantly (p less than 0.05) different for VT and N, but not for MI and N subjects. The significant (p less than 0.05) difference between MI and VT trajectory plots enabled 76% accuracy for MI and VT identification. ST-Tmax trajectory plots show significantly (p less than 0.0001) higher F values for VT patients facilitating accurate (87%) discrimination between MI and VT patients. These results suggest that the abnormalities of repolarization processes, displayed by MFM as multipolar integral of ST-T maps and/or as fragmented trajectory plots of ST-T extrema, may be useful indicators of the arrhythmia substrate/processes that characterize VT and vulnerable MI patients.

Complementary nature of electrocardiographic and magnetocardiographic data in patients with ischemic heart disease.

High resolution body surface potential maps (BSPM) and magnetic field maps (MFM) for study groups consisting of 11 Q wave and 11 non Q wave myocardial infarct (MI) patients as well as 9 normal subjects, were recorded in a magnetically and electrically shielded room. A control group of 22 normal subjects provided group mean normal time integral maps for selected QRST time intervals. The difference between magnitudes of extrema in each map defined the normal mean data range R for that time interval. The root mean square sum of the differences between the time integral map of a study subject and the normal group-mean map provided an estimate of individual map variability, V. Subsequent calculation of group-mean map variability, V, and group-mean normalized variability, V/R, for specific time intervals of the cardiac cycle, were used to test the abilities of BSPM and MFM techniques to distinguish between the normal and MI study groups. Results indicate that BSPM V/R differences between MI and normal groups are most pronounced during Q wave and Q zone activity; between inferior MI's and normals (p less than 0.05) and between anterior MI's and normal (p less than 0.01). Significant differences in MFM V/R occur during repolarization; between inferior MI's and non Q wave MI's (p less than 0.05), between anterior MI's and normals (p less than 0.05), between non Q wave MI's and normals (p less than 0.05) and between all MI's and normals (p less than 0.01). It is concluded that high resolution BSPM and MFM provide complementary means of discriminating between normal subjects and MI patients.