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dc.contributor.author | Huerta, Alvaro![]() |
es_ES |
dc.contributor.author | Martinez-Rodrigo, Arturo![]() |
es_ES |
dc.contributor.author | Rieta, J J![]() |
es_ES |
dc.contributor.author | Alcaraz, Raúl![]() |
es_ES |
dc.date.accessioned | 2021-12-20T08:39:23Z | |
dc.date.available | 2021-12-20T08:39:23Z | |
dc.date.issued | 2020-09-16 | es_ES |
dc.identifier.issn | 2325-887X | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/178571 | |
dc.description.abstract | [EN] A broad variety of algorithms for detection and classification of rhythm and morphology abnormalities in ECG recordings have been proposed in the last years. Although some of them have reported very promising results, they have been mostly validated on short and non-public datasets, thus making their comparison extremely difficult. PhysioNet/CinC Challenge 2020 provides an interesting opportunity to compare these and other algorithms on a wide set of ECG recordings. The present model was created by ¿ELBIT¿ team. The algorithm is based on deep learning, and the segmentation of all beats in the 12-lead ECG recording, generating a new signal for each one by concatenating sequentially the information found in each lead. The resulting signal is then transformed into a 2- D image through a continuous Wavelet transform and inputted to a convolutional neural network. According to the competition guidelines, classification results were evaluated in terms of a class-weighted F-score (Fß) and a generalization of the Jaccard measure (Gß). In average for all training signals, these metrics were 0.933 and 0.811, respectively. Regarding validation on the testing set from the first phase of the challenge, mean values for both performance indices were 0.654 and 0.372, respectively | es_ES |
dc.description.sponsorship | This research has been supported by the grants DPI2017¿83952¿C3 from MINECO/AEI/FEDER EU, SBPLY/17/180501/000411 from Junta de Comunidades de Castilla-La Mancha, AICO/2019/036 from Generalitat Valenciana and FEDER 2018/11744 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | IEEE | es_ES |
dc.relation.ispartof | CinC 2020. Computing in Cardiology, vol. 47 | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject.classification | TECNOLOGIA ELECTRONICA | es_ES |
dc.title | A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs | es_ES |
dc.type | Comunicación en congreso | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.22489/CinC.2020.305 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-83952-C3-1-R/ES/ESTUDIO MULTICENTRICO PARA LA EVALUACION DEL SUSTRATO ARRITMOGENICO EN PACIENTES CON FIBRILACION AURICULAR. APLICACION A LA ABLACION POR CATETER/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/JCCM//SBPLY%2F17%2F180501%2F000411//Caracterización del sustrato auricular mediante análisis de señal como herramienta de asistencia procedimental en ablación por catéter de fibrilación auricular/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/FEDER//2018%2F11744/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement///AICO%2F2019%2F036//METODOS DE DIAGNOSTICO Y TERAPIA PERSONALIZADA EN ABLACION POR CATETER DE ARRITMIAS CARDIACAS/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica | es_ES |
dc.description.bibliographicCitation | Huerta, A.; Martinez-Rodrigo, A.; Rieta, JJ.; Alcaraz, R. (2020). A Deep Learning Solution for Automatized Interpretation of 12-Lead ECGs. IEEE. 1-4. https://doi.org/10.22489/CinC.2020.305 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.conferencename | 47th Computing in Cardiology Conference (CinC 2020) | es_ES |
dc.relation.conferencedate | Septiembre 13-16,2020 | es_ES |
dc.relation.conferenceplace | Rimini, Italia | es_ES |
dc.relation.publisherversion | https://doi.org/10.22489/CinC.2020.305 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 4 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.pasarela | S\433022 | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | Junta de Comunidades de Castilla-La Mancha | es_ES |