Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/43948
Campo DC Valoridioma
dc.contributor.authorYadav, Anjalien_US
dc.contributor.authorDutta, Malay Kishoreen_US
dc.contributor.authorTravieso González, Carlos Manuelen_US
dc.contributor.authorAlonso, Jesus B.en_US
dc.date.accessioned2018-11-21T19:05:11Z-
dc.date.available2018-11-21T19:05:11Z-
dc.date.issued2018en_US
dc.identifier.isbn9781538675069en_US
dc.identifier.urihttp://hdl.handle.net/10553/43948-
dc.description.abstractCardiovascular diseases are very common these days and there arises a need for regular diagnosis of humans. Phonocardiogram is an effective diagnostic tool for analysing the heart sound. It helps in providing better information regarding clinical condition of the heart. This paper proposes an algorithmic method for differentiating a normal heart sound from an abnormal one using the PCG sound data. Cepstrum analysis has been performed on both types of signals and features are extracted from the heart sound. The extracted features are trained and tested with the help of a support vector machine classifier. The proposed method has achieved an accuracy of 95% in correctly classifying a heart sound PCG signal as normal and abnormal.en_US
dc.languagespaen_US
dc.relation.ispartof2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedingsen_US
dc.source2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings (8464131)en_US
dc.subject3307 Tecnología electrónicaen_US
dc.subject.otherHeart , Feature extraction , Phonocardiography , Band-pass filters , Cepstrum , Classification algorithms , Fourier transforms, Heart Sound , PCG , Signal Processing , Feature extraction , SVMen_US
dc.titleAutomatic Classification of Normal and Abnormal PCG Recording Heart Sound Recording Using Fourier Transformen_US
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dc.typeConferenceObjectes
dc.relation.conference2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018
dc.identifier.doi10.1109/IWOBI.2018.8464131
dc.identifier.scopus85054508369
dc.contributor.authorscopusid57195513394
dc.contributor.authorscopusid35291803600
dc.contributor.authorscopusid57196462914
dc.contributor.authorscopusid24774957200
dc.identifier.issue8464131-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.identifier.conferenceidevents121634
dc.identifier.ulpgces
item.grantfulltextnone-
item.fulltextSin texto completo-
crisitem.event.eventsstartdate18-07-2018-
crisitem.event.eventsenddate20-07-2018-
crisitem.author.deptGIR IDeTIC: 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.deptGIR IDeTIC: 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.orcid0000-0002-7866-585X-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameTravieso González, Carlos Manuel-
crisitem.author.fullNameAlonso Hernández, Jesús Bernardino-
Colección:Actas de congresos
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