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| Research article summary (published 29 Apr 2002): |
Automatic pattern recognition in ECG time series.
Full Abstract
In this paper, a technique for the automatic detection of any recurrent pattern in ECG time series is introduced. The wavelet transform is used to obtain a multiresolution representation of some example patterns for signal structure extraction. Neural Networks are trained with the wavelet transformed templates providing an efficient detector even for temporally varying patterns within the complete time series. The method is also robust against offsets and stable for signal to noise ratios larger than one. Its reliability was tested on 60 Holter ECG recordings of patients at the Department of Cardiology (University of Bonn). Due to the convincing results and its fast implementation the method can easily be used in clinical medicine. In particular, it solves the problem of automatic P wave detection in Holter ECG recordings.
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Author information
Author/s: Sternickel, Karsten (K);
Affiliation: Institut für Strahlen-und Kernphysik, University of Bonn, Nussallee 14-16, D-53115 Bonn, Germany. stern@iskp.uni-bonn.de
Journal and publication information
Publication Type: Journal Article
Journal: Computer methods and programs in biomedicine (Comput Methods Programs Biomed), published in Ireland. (Language: eng)
Reference: 2002-May; vol 68 (issue 2) : pp 109-15
Dates: Created 2002/04/04; Completed 2002/07/09; Revised 2004/11/17;
PMID: 11932027, status: MEDLINE (last retrieval date: 11/6/2008)
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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