Automatic pattern recognition in ECG time series.
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.
Sternickel
Karsten
K
Institut für Strahlen-und Kernphysik, University of Bonn, Nussallee 14-16, D-53115 Bonn, Germany. stern@iskp.uni-bonn.de
D004562:Electrocardiography / Q000706:statistics & numerical data*; D015716:Electrocardiography, Ambulatory / Q000706:statistics & numerical data; D006801:Humans; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012984:Software