Automated ECG diagnostic P-wave analysis using wavelets

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Title Automated ECG diagnostic P-wave analysis using wavelets
Author Diery, Adrian Phillip; Rowlands, David Duanne; Cutmore, Timothy; James, Daniel Arthur
Journal Name Computer Methods and Programs in Biomedicine
Year Published 2011
Place of publication Ireland
Publisher Elsevier
Abstract P-wave characteristics in the human ECG are an important source of information in the diagnosis of atrial conduction pathology. However, diagnosis by visual inspection is a difficult task since the P-wave is relatively small and noise masking is often present. This paper introduces novel wavelet characteristics derived from the continuous wavelet transform (CWT) which are shown to be potentially effective discriminators in an automated diagnostic process. Characteristics of the 12-lead ECG P-wave were derived using CWT and statistical methods. A normal control group and an abnormal (atrial conduction pathology) group were compared. The wavelet characteristics captured frequency, magnitude and variance components of the P-wave. The best individual characteristics (i.e. ones that significantly discriminated the groups) were entered into a linear discriminant analysis (LDA) for four different models: two-lead ECG, three-lead ECG, a derived three-lead ECG and a factor analysis solution consisting of wavelet characteristic loadings on the factors. A comparison was also made between wavelet characteristics derived form individual P-waves verses wavelet characteristics derived from a signal-averaged P-wave for each participant. These wavelet models were also compared to standard cardiological measures of duration, terminal force and duration divided by the PR segment. Results for the individual P-wave approach generally outperformed the standard cardiological measures and the signal-averaged P-wave approach. The best wavelet model on the basis of both classification performance and simplicity was the two-lead model that uses leads II and V1. It was concluded that the wavelet approach of automating classification is worth pursuing with larger samples to validate and extend the present study.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1016/j.cmpb.2010.04.012
Volume 101
Issue Number 1
Page from 33
Page to 43
ISSN 0169-2607
Date Accessioned 2010-12-21
Date Available 2013-05-29T03:14:52Z
Language en_US
Research Centre Behavioural Basis of Health; Griffith Health Institute
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Biomedical Engineering
URI http://hdl.handle.net/10072/37508
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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