Key result
Ventricular electrogram onset analysis using support vector methods was developed and tested to discriminate between supraventricular and ventricular tachycardias.
Why the study?
Does ventricular electrogram onset analysis using support vector machines improve discrimination between SVT and VT?
Observational (n=16)
Does ventricular electrogram onset analysis using support vector machines improve discrimination between SVT and VT?
Analysis of ventricular electrogram onset using support vector machines offers a potential algorithmic approach to better discriminate between supraventricular and ventricular tachycardias.
May aid SVT-VT discrimination in devices; leaves open prospective validation before clinical adoption.
We hypothesize that the analysis of the ventricular electrogram onset (EGM onset) can discriminate between SVT and VT to obtain a simultaneous increase in sensitivity and specificity. We discuss our analysis of EGMs obtained during SVT and VT together with their preceding SRs in 38 SVT and 68 VT far field records from 16 patients. The, algorithmic implementation and the preprocessing tasks were performed through the support vector method (SVM), avoiding the overfitting by means of the statistical bootstrap resampling. To improve the safety for an individual patient, two new methods of incremental learning, based on the SVM, are proposed and tested on an independent set of spontaneous arrhythmia episodes.
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Rojo‐Álvarez et al. (2002) conducted an observational in Supraventricular and ventricular tachycardias (n=16). Ventricular electrogram onset (EGM onset) analysis was evaluated on Discrimination between SVT and VT. Ventricular electrogram onset analysis using support vector methods was developed and tested to discriminate between supraventricular and ventricular tachycardias.
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