Classification of endocardial electrograms using adapted wavelet packets and neural networks

The discrimination of ventricular tachycardias with 1:1 retrograde conduction from sinus tachycardia still remains a challenge for rate based algorithms commonly used in dual-chamber implantable cardioverter defibrillators. Morphology based analysis techniques for a classification of antegrade and retrograde atrial activation patterns can be used to cope with this problem. Here time-domain template matching techniques are known approaches. However, a time-domain representation of endocardial electrograms is not optimal for classification tasks as the dimensionality of the underlying signal space is high and features being irrelevant for a signal characterization are involved in the analysis. Therefore, the aim of this study is to develop an enhanced morphological analysis tool for a classification of antegrade and retrograde atrial activation by using a transform domain representation of endocardial electrograms. For this, we applied an adapted wavelet-packet decomposition to extract discriminating features in endocardial electrograms representing antegrade and retrograde activation patterns. Further, a feed-forward neural network was utilized to produce a classification based on the extracted information. In using our hybrid method, no false classification of the physiological and pathological cardiac state was made. It is concluded that the proposed classification scheme represents a highly efficient approach for a classification of antegrade and retrograde atrial activation.