Electrocardiogram (ECG) particular from tiny, non Q-wave myocardial infarction may lack striking infarct pattern. Spatiotemporal correlation analysis (SCA) of multichannel magnetocardiogram (MCG) is a high-resolution "magnifying glass" to analyze homogeneity of repolarization. SCA involves full 4D spatiotemporal information to identify abnormalities as empirically done by eye in conventional ECG-but on an advanced level of analysis. We compared the discriminatory performance of SCA to ECG analysis in identifying myocardial infarction. Eleven SCA parameters were taken from signal averaged 31-channel MCG and compared to infarct indicators of ECG's in 178 subjects: 108 patients (76 males, mean age 62 years) after myocardial infarction (16-64 d) and 70 controls (36 males, mean age 46 years). SCA improves the detection of myocardial injury: in 72.5% ECG (sensitivity 68.6%, specificity 56%) and in 80.2% SCA parameters (sensitivity 72.6%, specificity 64%) separated patients from controls. SCA is applicable for the analysis of de- and repolarization of cardiac mapping data.

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.