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dc.contributor.authorŘedina, Richard
dc.date.accessioned2020-04-16T07:19:26Z
dc.date.available2020-04-16T07:19:26Z
dc.date.issued2019cs
dc.identifier.citationProceedings of the 25st Conference STUDENT EEICT 2019. s. 42-45. ISBN 978-80-214-5735-5cs
dc.identifier.isbn978-80-214-5735-5
dc.identifier.urihttp://hdl.handle.net/11012/186613
dc.description.abstractThe study is focused on a design of a reliable approach for ECG cycles clustering. It would be helpful for automatic assessment of various pathological patterns in ECG. Proposed method was tested and tuned on real data from ambulatory ECG database. The algorithm comprises ECG preprocessing, adjustment of R-peak positions available in database, creation of a template cycle, computation of features mainly representing correlation between particular cycles and the template, and, clustering of cycles within ECG via k-means. The appropriate number of clusters is derived via analysis of silhouette values. Resulting success of the algorithm in comparison with available manual scoring is: Sensitivity = 0.55 and Specificity=0.94.en
dc.formattextcs
dc.format.extent42-45cs
dc.format.mimetypeapplication/pdfen
dc.language.isocscs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 25st Conference STUDENT EEICT 2019en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subjectECGen
dc.subjectQRS complexen
dc.subjectcardiac arrhythmiasen
dc.subjectcluster analysisen
dc.subjectk-meansen
dc.subjectcorrelationen
dc.subjectsilhouetteen
dc.titleClustering Of Ecg Cyclesen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date25.04.2019cs
but.event.titleStudent EEICT 2019cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionPublishers's versionen


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