Ítem
Solo Metadatos
Neonatal seizure detection using deep convolutional neural networks
| dc.creator | Caicedo Dorado, Alexander | |
| dc.creator | Ansari, Amir H | spa |
| dc.creator | Cherian, Perumpillichira J | spa |
| dc.creator | Naulaers, Gunnar | spa |
| dc.creator | De Vos, Maarten | spa |
| dc.creator | Van Huffel, Sabine | spa |
| dc.date.accessioned | 2020-08-19T14:39:56Z | |
| dc.date.available | 2020-08-19T14:39:56Z | |
| dc.date.created | 2018-04-02 | spa |
| dc.description | La identificación de un conjunto básico de características es uno de los pasos más importantes en el desarrollo de un detector automático de ataques. En la mayoría de los estudios publicados que describen características y clasificadores de convulsiones, las características fueron diseñadas a mano, lo que puede no ser óptimo. El objetivo principal del presente artículo es utilizar redes neuronales convolucionales profundas (CNN) y bosques aleatorios para optimizar automáticamente la selección y clasificación de características | spa |
| dc.description.abstract | Identifying a basic set of characteristics is one of the most important steps in developing an automatic attack detector. In most of the published studies describing seizure characteristics and classifiers, the characteristics were designed by hand, which may not be optimal. The main objective of this article is to use deep convolutional neural networks (CNN) and random forests to automatically optimize the selection and classification of features | spa |
| dc.format.mimetype | application/pdf | |
| dc.identifier.doi | https://doi.org/10.1142/S0129065718500119 | |
| dc.identifier.issn | ISSN: 0129-0657 | |
| dc.identifier.uri | https://repository.urosario.edu.co/handle/10336/26634 | |
| dc.language.iso | eng | spa |
| dc.publisher | World Scientific and Engineering Academy and Society | spa |
| dc.relation.citationIssue | No. 4 | |
| dc.relation.citationTitle | International Journal of Neural Systems | |
| dc.relation.citationVolume | Vol. 29 | |
| dc.relation.ispartof | International Journal of Neural Systems, ISSN: 0129-0657, Vol.29, No.4 (2018) | spa |
| dc.relation.uri | https://www.worldscientific.com/doi/abs/10.1142/S0129065718500119 | spa |
| dc.rights.accesRights | info:eu-repo/semantics/restrictedAccess | |
| dc.rights.acceso | Restringido (Acceso a grupos específicos) | spa |
| dc.source | International Journal of Neural Systems | spa |
| dc.source.instname | instname:Universidad del Rosario | |
| dc.source.reponame | reponame:Repositorio Institucional EdocUR | |
| dc.subject | Redes neuronales profundas | spa |
| dc.subject | Red neuronal convolucional | spa |
| dc.subject | Detección de convulsiones neonatales | spa |
| dc.subject | Bosque aleatorio. | spa |
| dc.subject.keyword | Deep Neural Networks | spa |
| dc.subject.keyword | Convolutional Neural Network | spa |
| dc.subject.keyword | Neonatal Seizure Detection | spa |
| dc.subject.keyword | Random Forest. | spa |
| dc.title | Neonatal seizure detection using deep convolutional neural networks | spa |
| dc.title.TranslatedTitle | Detección de convulsiones neonatales mediante redes neuronales convolucionales profundas | spa |
| dc.type | article | eng |
| dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | |
| dc.type.spa | Artículo | spa |
