Hybrid wavelet packet-support vector classification of atrial activation patterns

The discrimination of ventricular tachycardia (VT) with 1:1 retrograde conduction from sinus tachycardia often remains a challenge for rate-based arrhythmia recognition algorithms commonly used in dual-chamber implantable cardioverter defibrillators (ICDs). In this study, we propose hybrid wavelet packet-support vector classifiers for the recognition of atrial activation patterns as rate independent approach. Consecutive beats representing antegrade (AA) and retrograde atrial (RA) activation patterns within data segments of 10s duration were supplied to a binary hybrid wavelet packet-support vector classifier with radial kernel and morphologically adapted wavelet packets followed by a regularization scheme to exclude ectopic beats and artifacts. This system utilizes an optimal representation of the EE waveforms and inherently minimizes an upper bound on the generalization error All data segments of an independent test set with AA and RA sequences Were classified correctly by the proposed scheme. The possible use of this scheme in dual-chamber ICDs may increase the specificity of the VT detection by these devices.