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dc.contributor.authorREILLY, RICHARD
dc.date.accessioned2008-07-26T09:43:40Z
dc.date.available2008-07-26T09:43:40Z
dc.date.createdJuly 23-28,2000,en
dc.date.issued2002
dc.date.submitted2002en
dc.identifier.citationdeChazal, P., Reilly, R.B., Using wavelet coefficients for the classification of the electrocardiogram, proceedings of the World Congress on Medical Physics and Biomedical Engineering: Chicago, 2000, pp64-67en
dc.identifier.otherY
dc.identifier.other52203
dc.identifier.otherYen
dc.identifier.urihttp://hdl.handle.net/2262/19542
dc.descriptionPUBLISHEDen
dc.description.abstractThis study investigates the automatic classification of the Frank lead electrocardiogram (ECG) into different pathophysiological disease categories. Coefficients from the discrete wavelet transform are used to represent the ECG diagnostic information and a comparison of the performance of classifiers processing feature sets generated using different mother wavelets is made. Fifteen feature sets are calculated from three Daubechies wavelets, with the decomposition level varied between 3 and 7. The classification performance of each feature set was optimised using automatic feature selection and by combining classifications of multi-beat ECG information. Throughout the study a database-of 500 ECG records with examples from seven disease categories was used. The classification of each record is known with 100% confidence and is based on ECG independent information. Using multiple runs of 10-fold cross-validation to obtain all results, it was shown that the overall classification performance of the different feature sets was 71.6-74.2%. In addition, the wavelet order and level had little influence on the overall performance. Analysis of the automatically chosen features reveal that time-frequency bands in the vicinity of the QRS onset and the T-wave are consistently selected.en
dc.format.extent64-67en
dc.format.extent463830 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherIEEEen
dc.rightsYen
dc.subjectelectrocardiogram classificationen
dc.subjectECG classificationen
dc.subjectWaveletsen
dc.subjectCross-validationen
dc.titleUsing wavelet coefficients for the classification of the electrocardiogramen
dc.title.alternativeWorld Congress on Medical Physics and Biomedical Engineeringen
dc.typeConference Paperen
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/reillyri
dc.identifier.rssurihttp://ieeexplore.ieee.org/iel5/7218/19483/00900669.pdf?arnumber=900669


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