DECONVOLUTION - A NOVEL SIGNAL-PROCESSING APPROACH FOR DETERMINING ACTIVATION TIME FROM FRACTIONATED ELECTROGRAMS AND DETECTING INFARCTED TISSUE

Background Two important signal processing applications in electrophys iology are activation mapping and characterization of the tissue subst rate from which electrograms are recorded. We hypothesize that a novel signal-processing method that uses de convolution is more accurate th an amplitude, derivative, and manual activation time estimates. We fur ther hypothesize that deconvolution quantifies changes in morphology t hat detect electrograms recorded from regions of myocardial infarction . Methods and Results To determine the accuracy of activation time est imation, 600 unipolar electrograms were calculated with a detailed com puter model using various degrees of coupling heterogeneity to model i nfarction. Local activation time was defined as the time of peak inwar d sodium current in the modeled myocyte closest to the electrode. Deco nvolution, minimum derivative, and maximum amplitude were calculated. Two experienced electrophysiologists blinded to the computer-determine d activation times marked their estimates of activation lime. F tests compared the variance of activation time estimation for each method. T o evaluate the performance of deconvolution to detect infarction, 380 unipolar electrograms were recorded from 10 dogs with infarcts resulti ng from ligation of the left anterior descending coronary artery. The amplitude, duration, number of inflections, peak frequency, bandwidth, minimum derivative, and deconvolution were calculated. Metrics were c ompared by Mann-Whitney rank-sum tests, and receiver operating curves were plotted. Conclusions. Deconvolution estimated local activation ti me more accurately than the other metrics (P<.0001). Furthermore, the algorithm quantified changes in morphology (P<.0001) with superior per formance, detecting electrograms recorded from regions of myocardial i nfarction. Thus, deconvolution, which incorporates a priori knowledge of electrogram morphology, shows promise to improve present clinical m etrics.