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Multichannel magnetocardiographic (MCG) recordings with fixed sensor arrays are not directly comparable with single-channel measurements carried out at standard grid locations. In addition, comparison of data obtained with different types of magnetometers is difficult. We present a method for transforming multichannel measurements to the standard-grid format. The minimum-norm estimate (MNE) of the source current distribution in the body is calculated, and the desired field components in standard grid points are then computed from the MNE. We measured three subjects with both a 24-channel and a single-channel instrument. The signals extrapolated from the multichannel measurements corresponded quite well to the single-channel data registered at the standard grid locations, especially in those grid points that were covered by the 24-channel device. The signal-amplitude-weighted correlations between the extrapolated and directly measured signals were 0.73-0.87. In simulations with ideal measurement geometry but with a realistic amount of random noise in the signals, we obtained a 0.99 correlation. It was also found that the method is relatively tolerant to errors in the location and orientation of the multichannel magnetometer. For example, a simulated 20-mm displacement in the location of the sensor array caused only a 3% decrease in the correlation, and when it was rotated and tilted by 10 [degrees], the correlation decreased by 5%. The basic advantage of our extrapolation method is its physiologic nature: the method is based on the mathematical modeling of the source current distribution, rather than on direct constraints applied to the magnetic field.

Abstract: Magnetocardiography (MCG) is a method complementary to electrocardiography (ECG). We examined recording and reproducibility of atrial depolarization signal by MCG. Multichannel MCG over anterior chest and orthogonal 3-lead ECG were recorded in 9 patients who had paroxysmal lone atrial fibrillation and in 10 healthy subjects in duplicate at least 1 week apart. Data were averaged using atrial wave template and high-pass filtered at 25, 40, and 60 Hz. Atrial signal duration with automatic detection of onset and offset and root mean square amplitudes of the last portion of atrial signal were determined. Coefficient of variation of atrial signal duration by MCG at 40 Hz was 3.3% and difference between the measurements was 3.5 milliseconds on average. The corresponding figures obtained by signal-averaged ECG (SAECG) were 6.1% and 6.9 milliseconds. Coefficient of variation for root mean square of the last 40 milliseconds of atrial signal were 16% in MCG and 17% in SAECG. Reproducibility was best at 40-Hz filter and similar in patients and healthy subjects. In conclusion, the reproducibility of atrial signal variables in MCG is adequate and somewhat better than in SAECG and equal in patients with lone atrial fibrillation and healthy subjects. Magnetocardiography seems to be a potentially valuable method to evaluate features of atrial depolarization in patient studies. [Copyright &y& Elsevier]

Introduction: Experimentally, both delayed ventricular conduction and nonhomogeneous ventricular repolarization contribute to reentrant arrhythmias. We tested the hypothesis that increased T wave dispersion is independent of delayed ventricular conduction associated with arrhythmia vulnerability in postmyocardial infarction (post-MI) patients. Methods and Results: We studied 32 post-MI patients with clinical or inducible monomorphic ventricular tachycardia (VT group), 28 post-MI patients without arrhythmias (MI group), and 13 healthy controls, using magnetocardiographic (MCG) mapping with signal averaging. Twelve-lead ECG was the reference. Filtered QRS duration (fQRS) and T wave peak to T wave end interval (TPE) were used as measures of ventricular conduction and nonhomogeneity in ventricular repolarization, respectively. In MCG, the VT group showed the longest fQRS (135 ± 34 msec vs 114 ± 22 msec in the MI group: P = 0.012). Mean TPE and maximum TPE in VT versus MI groups were 78 ± 9 msec versus 70 ± 6 msec (P < 0.001) and 117 ± 23 msec versus 104 ± 19 msec (P = 0.020), respectively. Maximum TPE did not correlate with fQRS in the VT group (r = 0.063; P = NS) but did correlate in the MI group (r = 0.396; P = 0.037). For identification of post-MI patients prone to VT, selection of cutoff values for fQRS > 140 msec and mean TPE >81 msec gave sensitivity and specificity of 41% and 89%, and 31% and 96%, respectively. Their combination increased sensitivity to 63% while maintaining 89% specificity. Conclusion: Post-MI patients susceptible to VT show increased T wave dispersion independent of delayed ventricular conduction. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate if magnetocardiographic (MCG) analysis of cardiac micropotentials within the QRS complex can identity patients prone to ventricular arrhythmias, and to compare it to MCG time-domain, late-field analysis. The study population consisted of 136 patients with remote MI, 53 with and 83 without a history of VT. After averaging and high pass filtering of multichannel MCG signals, time-domain parameters describing the end-QRS activity and fragmentation index M and score S describing the whole QRS complex were computed. Fragmentation and time-domain parameters differed between the VT and control groups: fragmentation index M was 12 ± 3 versus 9 ± 2 (P < 0.001), fragmentation score S was 83 ± 42 versus 56 ± 21 (P < 0.001), and filtered QRS duration was 144 ± 32 versus 114 ± 19 ms (P < 0.001) in VT and control groups, respectively. A combination of fragmentation parameters yielded 87% sensitivity and 61 % specificity in VT identification. Corresponding figures for a time-domain parameter combination were 81% and 72%. Sensitivity of time-domain analysis was 88% and specificity was 75% in a subgroup with anterior Ml. In multivariate analysis, fragmentation and time-domain analyses discriminated VT patients from controls independently of the extent of coronary artery disease or left ventricular dysfunction. MCG in postinfarction patients reveals pathology associated with propensity to ventricular arrhythmias inside and not only at the end of the QRS complex. MGC seems most accurate in the anterior infarct location. [ABSTRACT FROM AUTHOR]

Introduction: Magnetocardiographic (MCG) mapping is a new method to record cardiac signals. This study examined the association of MCG late fields with the propensity to sustained ventricular tachycardia (VT) after myocardial infarction (MI). Methods and Results: One hundred patients with remote MI were studied, 38 with and 62 without history of VT. High-resolution MCG and signal-averaged ECG (SAECG) as a comparative method were recorded. Time-domain parameters describing the abnormal low-amplitude end QRS activity, MCG late fields, and SAECG late potentials were analyzed. Late field parameters differed significantly between the patient groups: filtered QRS duration was 137 ± 26 msec in the VT group and 110 ± 18 msec in the control group (P < 0.001), and root mean square amplitude of the last 40 msec was 260 ± 170 and 510 ± 360 fT (P < 0.001), respectively. The optimal MCG parameter combination yielded a sensitivity of 92% and a specificity of 61% in classification to the VT group, whereas those for SAECG were 63% and 66%. In a subgroup of 63 patients with marked left ventricular dysfunction and comparable stage of coronary heart disease, only MCG (sensitivity 73%, specificity 67%) but not SAECG could assign a patient to the VT group. Conclusion: Late fields of the MCG QRS complex indicate propensity to life-threatening arrhythmias in post-MI patients. This discriminative ability persists in the presence of severe left ventricular dysfunction where ECG late potentials lose their informative value. MCG late field analysis is a potential new method for noninvasive risk assessment in post-Ml patients. [ABSTRACT FROM AUTHOR]

Background: Detection of myocardial ischemia was studied with multichannel exercise magneto-cardiography (MCG). A surface gradient method was applied to analyze cardiac magnetic fields. Methods: We studied 27 patients with single vessel coronary artery disease (CAD) and 17 healthy volunteers. The MCG was recorded over anterior chest during supine bicycle ergometry. The two-dimensional direction of the maximum spatial magnetic field gradient was determined during the ST segment and at the T-wave apex at different phases of stress test. Results: The CAD patient group was separated from controls by the orientation of the magnetic field gradient during the ST segment at cessation of exercise (CAD 167 ± 68° vs controls 106 ± 49°; P < 0.005) and at 4 minutes postexercise, but not at rest. The-CAD patient group was separated from controls also by the orientation of the magnetic field gradient at the T-wave apex at 4 minutes postexercise (CAD 87 ± 60° vs controls 58 ± 18° P < 0.05), but not at rest. The change in the orientation of the field gradient at the T-wave apex 4 minutes postexercise, compared to baseline, was greater in CAD patients (31 ± 43°) than in controls (9 ± 8° P < 0.05). This change was larger in the patient group with stenosis in the right than in the left coronary artery (P < 0.05). Conclusions: Transient acute myocardial ischemia causes well-recognizable changes in the magnetocardiogram at the ST segment and the T wave. The orientation of the maximum spatial gradient of the magnetic field can be used as a parameter to determine these changes. [ABSTRACT FROM AUTHOR]

Focuses on thermal magnetic noise fields generated by slabs with high electrical conductivity of high magnetic permeability. Spectral densities and spatial correlations of magnetic field fluctuations; Estimation of thermal noise fields in the walls of a magnetically shielded room.

Background During recent years artificial neural networks have been proposed as a diagnostic tool in different fields of cardiology. Most of the studies have utilized the multilayer perceptron with backpropagation learning rule for the design of the network. As a new approach, Learning Vector Quantization (LVQ) which belongs to the class of competitive learning networks, was developed particularly for classification problems. So far there are no data available on the application of LVQ for classification tasks in cardiology. The present study aims at investigating the performance of LVQ for localization of myocardial infarction (Ml) based on ST elevations in the standard 12-lead ECG. Methods Altogether, 769 male patients (age 53 ± 7 years) with an acute Ml were included into the study. Three hundred fifty-three patients (46%) presented with anterior and 416 patients (54%) with inferior Ml based on typical changes in the standard 12-lead ECG. Standardized ST elevations in all 12 leads were used as input structure for the network. The performance of the network was studied using two different learning and test sets. The influence of the number of reference vectors and training steps on the classification accuracy for infarct location was investigated. Results The highest classification accuracy of 88.6% for infarct location was achieved using the learning set with 66% of all patients. This setup was based on five reference vectors and 200 training steps. The best accuracies for anterior Ml were higher as compared to inferior infarctions in both the test and training set. Using more than 50 reference vectors resulted in a decrease of classification accuracy due to overtraining of the network. Conclusion Appropriately initialized and trained artificial neural networks based on LVQ give a high accuracy for localization of Ml using only ST elevations of the standard 12-lead ECG. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study was to investigate the prognostic significance of the wavelet decomposition (WD), when applied to single leads of the signal-averaged ECG (SAECG) of postinfarction patients, as well as to compare it with conventional time-domain (TD) analysis in order to optimize the utility of high resolution ECG techniques in risk stratification. Comparisons with clinical variables were also performed. Methods: SAECGs of 769 patients were analyzed (mean age 53 ± 7 years). A total of 42 arrhythmic events occurred within 54 months. For numerical calculations of WD, relative energies of the QRS complex were obtained in seven frequency bands. Conventional parameters of late potential analysis in TD were calculated at 25 and 40 Hz high pass filter. Results: No significant correlation was found between parameters of both methods. Several parameters of the WD of single leads showed slightly higher sensitivity (e.g., 52% vs 36%) and positive predictive values (e.g., 13.3% vs 10.2%) compared with those obtained with the QRS duration of TD analysis. In multivariate analysis, combination of QRS duration with WD parameters (relative risk [rr] =4.55, P < 0.0001), followed by the WD of single leads (rr = 4.19, P < 0.0001), nonsustained ventricular tachycardia in the long-term ECG (rr = 3.72, P = 0.0005) and the history of previous infarction (rr = 2.72, P = 0.0035) were the most significant predictors in risk stratification. Conclusion: Time domain and wavelet transform of SAECG resulted in an improvement in postinfarction risk stratification compared with the clinical variables included in this study. Wavelet parameters may not be vastly superior to QRS duration, but they are a new index measuring intra-QRS phenomena. [ABSTRACT FROM AUTHOR]

QT dispersion is a measure of heterogeneity in ventricular repolarization. Increased ECG QT dispersion is associated with life-threatening ventricular arrhythmias. We studied if magnetocardiographic (MCG) measures of QT dispersion can separate postmyocardial infarction patients with and without susceptibility to sustained VT. Manual dispersion measurements were compared to a newly adopted automatic QT interval analysis method. Ten patients with a history of sustained VT (VT group) and eight patients without ventricular arrhythmias (Controls) were studied after a remote myocardial infarction. Single-channel MCGs were recorded from 42 locations over the frontal chest area and the signals were averaged. QT dispersion was defined as maximum - minimum or standard deviation of measured QT intervals. VT group showed significantly more QT and JT dispersion than Controls. QTapex dispersions were 127 ± 26 versus 83 ± 21 ms (P = 0.004) and QTend dispersions 130 ± 37 versus 82 ± 37 ms (P = 0.013), respectively. Automatic method gave comparable values. Their relative differences were 9% for QTapex and 27% for QTend dispersion on average. In conclusion, increased MCG QT interval dispersion seems to be associated with a susceptibility to VT in postmyocardial infarction patients. MCG mapping with automated QT interval analysis may provide a user independent method to detect nonhomogeneity in ventricular repolarization. [ABSTRACT FROM AUTHOR]