Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/48815
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dc.contributor.authorMurillo Rendón, Santiagoen_US
dc.contributor.authorHoyos, Cristian Castroen_US
dc.contributor.authorTravieso-Gonzales, Carlos M.en_US
dc.contributor.authorCastellanos-Domínguez, Germánen_US
dc.date.accessioned2018-11-24T01:10:58Z-
dc.date.available2018-11-24T01:10:58Z-
dc.date.issued2013en_US
dc.identifier.isbn9783642386817en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/10553/48815-
dc.description.abstractIn this paper, phonocardiography (PCG) segmentation methodology based on envelope detection is developed by using a time-scale representation and a synthetic electrocardiogram signal (EKG). The heart cycle duration is calculated by autocorrelation of S1-S2 sounds that are synchronized with the synthetic EKG. Two algorithms for noisy signal removal are implemented to ensure the detection of signals with low signal to noise ratio. Approach is tested in a PCG database holding 232 recordings. Results show an achieved accuracy up of 90%, thus, overperforming three state-of-the-art PCG segmentation techniques used to compare the proposed approach. Additionally, the synthetic EKG is built by estimation of heart rate length, thus it does not use a patient recording EKG, reducing the computational cost and the amount of required devices.en_US
dc.languageengen_US
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)[ISSN 0302-9743],v. 7903 LNCS, p. 124-134en_US
dc.subject3314 Tecnología médicaen_US
dc.subject.otherHeart Sound Segmentationen_US
dc.subject.otherPhonocardiogramen_US
dc.subject.otherTelemedicineen_US
dc.subject.otherAutocorrelationen_US
dc.titlePhonocardiography signal segmentation for telemedicine environmentsen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.typeConferenceObjecten_US
dc.relation.conference12th International Work-Conference on Artificial Neural Networks (IWANN)en_US
dc.identifier.doi10.1007/978-3-642-38682-4_15en_US
dc.identifier.scopus84880054260-
dc.identifier.isi000324899200015-
dc.contributor.authorscopusid54412836100-
dc.contributor.authorscopusid55791555500-
dc.contributor.authorscopusid6602376272-
dc.contributor.authorscopusid25640642900-
dc.description.lastpage134en_US
dc.description.firstpage124en_US
dc.relation.volume7903 LNCSen_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.contributor.daisngid32220678-
dc.contributor.daisngid5598506-
dc.contributor.daisngid265761-
dc.contributor.daisngid151115-
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Rendon, SM-
dc.contributor.wosstandardWOS:Hoyos, CC-
dc.contributor.wosstandardWOS:Travieso-Gonzales, CM-
dc.contributor.wosstandardWOS:Castellanos-Dominguez, G-
dc.date.coverdateJulio 2013en_US
dc.identifier.conferenceidevents120842-
dc.identifier.ulpgces
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptIDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-4621-2768-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameTravieso González, Carlos Manuel-
crisitem.event.eventsstartdate12-06-2013-
crisitem.event.eventsenddate14-06-2013-
Appears in Collections:Actas de congresos
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