Discrimination between monomorphic and polymorphic ventricular tachycardia using cycle length variability measured by wavelet transform analysis.

The objective of this study was to assess the capability of wavelet transform (WT) analysis to differentiate between monomorphic (MVTs) and polymorphic ventricular tachycardias (PVTs) in a canine model and to relate these results to epicardial isochronal maps on a beat-by-beat basis. Unipolar electrograms were simultaneously recorded from the surface of both ventricles with a 127-lead sock electrode array in 24 open-chest anesthetized dogs. The sampling frequency was 500 Hz. Atrioventricular block was induced by formaldehyde injection into the atrioventricular node. The left anterior descending coronary artery was occluded for 60 minutes under ventricular pacing (140 stimuli/min) followed by reperfusion. Ventricular tachycardias were obtained during reperfusion and during left stellate ganglion stimulation. After visual selection, a total of 97 segments of 2,048 samples (4.096 seconds) were extracted and classified as 67 MVTs and 30 PVTs. A parameter based on the cycle length variability was defined in the second scale of the WT decomposition, normalized by its mean value. Similar assessment of cycle length variability was performed based on the detection of the point of most rapid change in potential with a negative slope in excess of -0.5 mV/ms in each individual electrogram to test the accuracy of the results obtained with the WT parameter. The WT parameter correctly identified 97% MVT and 83.3% PVT segments, for an overall accuracy of 92.8%. Beat-by-beat epicardial maps of MVT displayed a cluster of sites of initial activation close to the reperfusion area, while the sites of breakthrough from beats during PVT were much more dispersed over both ventricles. A strong and significant correlation was found between the number of electrodes with the earliest epicardial activation and the WT parameter (r = .78, P < .0001). To test the accuracy of the results obtained, a comparison was performed between the WT parameter (0.082 +/- 0.007) and the cycle length variability, estimated as the normalized standard deviation of the intervals between individual electrograms (0.076 +/- 0.006). No significant differences were shown (P = .0022), and a strong linear correlation was found between both measurements (Pearson correlation coefficient, .966). It is concluded that WT analysis discriminated accurately between MVT and PVT, and a quantitative relation was found with the spatial dispersion of sites of earliest epicardial activation. The WT results strongly correlated with those obtained by another method of estimating cycle length variability. Methodologically, the strength of the WT lies in the complementary information that could be extracted from the processing of electrograms to enhance the detection/discrimination of different types of arrhythmias.