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Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers
dc.contributor.author | Mondéjar-Guerra, Víctor | |
dc.contributor.author | Novo Buján, Jorge | |
dc.contributor.author | Rouco, J. | |
dc.contributor.author | Penedo, Manuel | |
dc.contributor.author | Ortega Hortas, Marcos | |
dc.date.accessioned | 2023-12-13T15:18:29Z | |
dc.date.available | 2023-12-13T15:18:29Z | |
dc.date.issued | 2019-01 | |
dc.identifier.citation | Mondéjar-Guerra, V., Novo, J., Rouco, J., Penedo, M. G., & Ortega, M. (2019). Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers. Biomedical Signal Processing and Control, 47, 41–48. doi:10.1016/j.bspc.2018.08.007 | es_ES |
dc.identifier.issn | 1746-8094 | |
dc.identifier.uri | http://hdl.handle.net/2183/34480 | |
dc.description | ©2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/. This version of the article: Mondéjar-Guerra, V., Novo, J., Rouco, J., Penedo, M. G., & Ortega, M. (2019). “Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers” has been accepted for publication in Biomedical Signal Processing and Control, 47, 41–48. The Version of Record is available online at: https://doi.org/10.1016/j.bspc.2018.08.007. | es_ES |
dc.description.abstract | [Abstract]: A method for the automatic classification of electrocardiograms (ECG) based on the combination of multiple Support Vector Machines (SVMs) is presented in this work. The method relies on the time intervals between consequent beats and their morphology for the ECG characterisation. Different descriptors based on wavelets, local binary patterns (LBP), higher order statistics (HOS) and several amplitude values were employed. Instead of concatenating all these features to feed a single SVM model, we propose to train specific SVM models for each type of feature. In order to obtain the final prediction, the decisions of the different models are combined with the product, sum, and majority rules. The designed methodology approaches are tested on the public MIT-BIH arrhythmia database, classifying four kinds of abnormal and normal beats. Our approach based on an ensemble of SVMs offered a satisfactory performance, improving the results when compared to a single SVM model using the same features. Additionally, our approach also showed better results in comparison with previous machine learning approaches of the state-of-the-art. | es_ES |
dc.description.sponsorship | This work was partially supported by the Research Project RTC-2016-5143-1, financed by the Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (ERDF). Also, this work has received financial support from the ERDF and the Xunta de Galicia, Centro singular de investigación de Galicia accreditation 2016–2019, Ref. ED431G/01; and Grupos de Referencia Competitiva, Ref. ED431C 2016-047. | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2016-047 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation | info:eu-repo/grantAgreement/MINECO//RTC-2016-5143-1/ES/eDSalud, Plataforma de Telemedicina Multiespecialidad | es_ES |
dc.relation.isversionof | https://doi.org/10.1016/j.bspc.2018.08.007 | |
dc.relation.uri | https://doi.org/10.1016/j.bspc.2018.08.007 | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC-BY-NC-ND 4.0) | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject | Electrocardiogram (ECG) | es_ES |
dc.subject | Heartbeat classification | es_ES |
dc.subject | Support vector machine (SVM) | es_ES |
dc.subject | Combining classifiers | es_ES |
dc.subject | Ensemble of classifiers | es_ES |
dc.title | Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | Biomedical Signal Processing and Control | es_ES |
UDC.volume | 47 | es_ES |
UDC.startPage | 41 | es_ES |
UDC.endPage | 48 | es_ES |
dc.identifier.doi | 10.1016/j.bspc.2018.08.007 |
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