NDLI: Feasibility of wavelet-kernel novelty detectors for identifying ventricular tachycardia

Content Provider	IEEE Xplore Digital Library
Author	Strauss, D.J. Jung, J.
Copyright Year	2003
Description	Author affiliation: Key Numerics, Saarbrucken, Germany (Strauss, D.J.)
Abstract	Hybrid wavelet-support vector classifiers have recently been suggested as efficient rate independent recognition scheme for endocardial electrograms (EEs) usable in implantable cardioverter defibrillators (ICDs). However, these schemes involve waveforms of the normal sinus rhythm (NSR) as well as the pathological rhythm. In this study, we assess the feasibility of hybrid wavelet-kernel novelty detectors to discriminate between NSR and ventricular tachycardia (VT). These schemes only involve waveforms of NSR for the learning task. Consecutive beats were selected as morphological patterns of NSR and VT. These patterns were represented by their multilevel concentrations using a lattice structure based wavelet decomposition. The lattice angles were optimized for kernel based novelty detectors. In a blind analysis of the independent test set, our scheme outperformed the correlation waveform analysis with best fit alignment which is a well accepted method for arrhythmia recognition. However, the performance of wavelet-support vector classifiers was not achievable. We conclude that wavelet-kernel novelty detectors are feasible for the rate independent identification of VTs. Our scheme is of a low complexity and suitable for efficient real-time implementations. However, further studies are needed to evaluate their performance in the clinical practice.
Sponsorship	European Soc. of Cardiology Nat. Inst. of Health, USA
Starting Page	529
Ending Page	532
File Size	213173
Page Count	4
File Format	PDF
ISBN	078038170X
ISSN	02766547
DOI	10.1109/CIC.2003.1291209
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-09-21
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Detectors Rhythm Lattices Cardiology Kernel Filter bank Pathology Medical treatment Hospitals Testing
Content Type	Text
Resource Type	Article

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