Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/342106
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.editorInstitute of Electrical and Electronics Engineers-
dc.identifier.isbn978-1-5386-6376-9pt_BR
dc.contributor.authorunicampOliveira, Alexandre Tomazati-
dc.contributor.authorunicampNóbrega, Eurípedes Guilherme de Oliveira-
dc.typeArtigopt_BR
dc.titleA novel arrhythmia classification method based on convolutional neural networks interpretation of electrocardiogram imagespt_BR
dc.contributor.authorOliveira, Alexandre Tomazati-
dc.contributor.authorNobrega, Euripedes G. O.-
dc.subjectOperadores de convoluçãopt_BR
dc.subjectEletrocardiografiapt_BR
dc.subjectRedes neurais (Computação)pt_BR
dc.subject.otherlanguageConvolution operatorspt_BR
dc.subject.otherlanguageElectrocardiographypt_BR
dc.subject.otherlanguageNeural networks (Computer science)pt_BR
dc.description.abstractA new method for classifying cardiac abnormalities is here proposed based on the electrocardiogram (ECG). The ECG may manifest abnormal heart patterns, which are generally known as arrhythmias. MIT-BIH arrhythmia database and AAMI standards are used for machine learning purposes considering the patient-oriented scheme. Heartbeat time intervals and morphological features processed by a 2-D time-frequency wavelet transform of ECG signals are combined into an image, which carries relevant information from each heartbeat. These dataset images are used as input to train and evaluate the classifier, which is essentially a 6 layers convolutional neural network (CNN), resulting in powerful artifact discrimination. The training set is artificially augmented to reduce the imbalance of the five heartbeat classes, achieving better results. A significant achieved overall accuracy of 95.3% of the proposed method, compared to some of the most relevant published methods, permits to expect effective results when applied to real patientspt_BR
dc.relation.ispartofIEEE International conference on industrial technologypt_BR
dc.publisher.cityPiscataway, NJpt_BR
dc.publisher.countryEstados Unidospt_BR
dc.date.issued2019-
dc.date.monthofcirculationJulypt_BR
dc.language.isoengpt_BR
dc.rightsFechadopt_BR
dc.sourceSCOPUSpt_BR
dc.identifier.issn2643-2978pt_BR
dc.identifier.eissn2643-2978pt_BR
dc.identifier.doi10.1109/ICIT.2019.8755177pt_BR
dc.identifier.urlhttps://ieeexplore.ieee.org/document/8755177pt_BR
dc.date.available2020-05-27T15:31:57Z-
dc.date.accessioned2020-05-27T15:31:57Z-
dc.description.provenanceSubmitted by Susilene Barbosa da Silva (susilene@unicamp.br) on 2020-05-27T15:31:57Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-08-27T19:18:07Z : No. of bitstreams: 1 2-s2.0-85069041050.pdf: 244550 bytes, checksum: 600bda75fe1cb33355d66a6ddace4929 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-05-27T15:31:57Z (GMT). No. of bitstreams: 0 Previous issue date: 2019en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/342106-
dc.description.conferencenomeIEEE International conference on industrial technology (ICIT)pt_BR
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.contributor.unidadeFaculdade de Engenharia Mecânicapt_BR
dc.subject.keywordClassification (of information)pt_BR
dc.identifier.source2-s2.0-85069041050pt_BR
dc.creator.orcid0000-0002-6005-505Xpt_BR
dc.creator.orcidSem informaçãopt_BR
dc.type.formArtigo de eventopt_BR
dc.identifier.articleid18797864pt_BR
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