Key result
Myocardial electrogram evaluation reveals one common frequency domain feature despite highly variable optimal subspaces.
Why the study?
Implantable devices that terminate ventricular tachycardia require highly reliable heart rhythm classification, but the relative discriminating power of myocardial electrogram features is unclear.
A specific frequency domain feature may be consistently useful across patients for classifying heart rhythms in implantable devices, despite high inter-subject variability in optimal electrogram feature sets.
One frequency feature may aid consistent device-based rhythm classification despite variability; leaves open validation in larger cohorts before any clinical use.
Implantable devices that terminate ventricular tachycardia must be capable of correctly classifying heart rhythms to a high degree of reliability. We evaluated the relative discriminating power of several myocardial electrogram (ME) features in six human subjects by reducing the order of their corresponding feature spaces using three different optimization methods: 1) minimizing univariate Bayes error rates (univariate parametric), 2) maximizing the Kullback divergence (multivariate parametric), and 3) pruning classification trees (nonparametric). We found that although the composition of the optimal subspaces varied considerably from one subject to another, one frequency domain feature was common to most of the optimal subspaces.
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Gibb et al. (1993) studied Ventricular tachycardia (n=6). Myocardial electrogram (ME) feature selection was evaluated on Optimal subspace composition for discriminating heart rhythms. Evaluation of myocardial electrogram features in six subjects revealed that although optimal subspaces varied considerably, one frequency domain feature was common to most.
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