Methods for Improved Discrimination Between Ventricular Fibrillation and Tachycardia.

Abstract

Differentiating between ventricular tachycardia and ventricular fibrillation in clinical and preclinical research is based on subjective definitions that have yet to be validated using objective criteria. This is partly due to shortcomings in the discrimination ability of current objective approaches, typified by the algorithms that perform cardiac rhythm classification using low-dimensional feature representations of electrocardiogram (ECG) signals. These identify ventricular tachyarrhythmias, but do not discriminate between ventricular tachycardia and ventricular fibrillation. In order to address this limitation, we have tested the utility of high-dimensional feature vectors, in particular, magnitude spectra and classifier ensembles that take into account local context information from ECG signals. Using these approaches, we categorized rhythms into three classes: ventricular tachycardia, ventricular fibrillation, and any other possible rhythm, defined here as "nonventricular rhythms." The high-dimensional spectral features achieved a substantial improvement in the discrimination between ventricular tachycardia and ventricular fibrillation, but exhibited a decreased sensitivity to nonventricular rhythms. In order to deal with the reduced sensitivity for the detection of nonventricular rhythms, methods were elaborated for combining the strengths of different feature spaces, and this substantially improved the identification sensitivities of all three classes.