Show simple item record

dc.contributor.authorDiery, Adrianen_US
dc.contributor.authorRowlands, Daviden_US
dc.contributor.authorCutmore, Timothyen_US
dc.contributor.authorJames, Danielen_US
dc.date.accessioned2017-05-03T12:27:43Z
dc.date.available2017-05-03T12:27:43Z
dc.date.issued2011en_US
dc.date.modified2013-05-29T03:14:52Z
dc.identifier.issn01692607en_US
dc.identifier.doi10.1016/j.cmpb.2010.04.012en_US
dc.identifier.urihttp://hdl.handle.net/10072/37508
dc.description.abstractP-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.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherElsevieren_US
dc.publisher.placeIrelanden_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofpagefrom33en_US
dc.relation.ispartofpageto43en_US
dc.relation.ispartofissue1en_US
dc.relation.ispartofjournalComputer Methods and Programs in Biomedicineen_US
dc.relation.ispartofvolume101en_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchBiomedical Engineering not elsewhere classifieden_US
dc.subject.fieldofresearchcode090399en_US
dc.titleAutomated ECG diagnostic P-wave analysis using waveletsen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Engineeringen_US
gro.date.issued2011
gro.hasfulltextNo Full Text


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record