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IoT and fog computing-based monitoring system for cardiovascular patients with automatic ECG classification using deep neural networks

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IoT and fog computing-based monitoring system for cardiovascular patients with automatic ECG classification using deep neural networks

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dc.contributor.author Rincón-Arango, Jaime Andrés es_ES
dc.contributor.author Guerra-Ojeda, Solanye es_ES
dc.contributor.author Carrascosa Casamayor, Carlos es_ES
dc.contributor.author Julian, Vicente es_ES
dc.date.accessioned 2023-06-16T18:02:32Z
dc.date.available 2023-06-16T18:02:32Z
dc.date.issued 2020-12 es_ES
dc.identifier.uri http://hdl.handle.net/10251/194328
dc.description.abstract [EN] Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The system sends the ECG signal to a Fog layer service by using the LoRa communication protocol. Also, it includes an AI algorithm based on deep learning for the detection of Atrial Fibrillation and other heart rhythms. The automatic detection of arrhythmias can be complementary to the diagnosis made by the physician, achieving a better clinical vision that improves therapeutic decision making. The performance of the proposed system is evaluated on a dataset of 8.528 short single-lead ECG records using two merge MobileNet networks that classify data with an accuracy of 90% for atrial fibrillation. es_ES
dc.description.sponsorship This work was partly supported by the Spanish Government (RTI2018-095390-B-C31), Universitat Politecnica de Valencia Research Grant PAID-10-19. S.G-O has been funded by grant PDBCEx COLDOC 679, scholarship programme from COLCIENCIAS (Administrative Department of Science, Technology and Innovation of Colombia). es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Cardiovascular Diseases es_ES
dc.subject ECG es_ES
dc.subject IoT es_ES
dc.subject Fog-AI es_ES
dc.subject LoRa es_ES
dc.subject Edge-AI es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title IoT and fog computing-based monitoring system for cardiovascular patients with automatic ECG classification using deep neural networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s20247353 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095390-B-C31/ES/HACIA UNA MOVILIDAD INTELIGENTE Y SOSTENIBLE SOPORTADA POR SISTEMAS MULTI-AGENTES Y EDGE COMPUTING/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UPV//PAID-10-19//Mejora de prestaciones del páncreas artificial ante ingestas y ejercicio mediante observadores de perturbaciones y técnicas de compensación de retardos/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/COLCIENCIAS//COLDOC 679/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation Rincón-Arango, JA.; Guerra-Ojeda, S.; Carrascosa Casamayor, C.; Julian, V. (2020). IoT and fog computing-based monitoring system for cardiovascular patients with automatic ECG classification using deep neural networks. Sensors. 20(24):1-19. https://doi.org/10.3390/s20247353 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/s20247353 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 19 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 20 es_ES
dc.description.issue 24 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 33371514 es_ES
dc.identifier.pmcid PMC7767482 es_ES
dc.relation.pasarela S\424213 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
dc.contributor.funder Departamento Administrativo de Ciencia, Tecnología e Innovación, Colombia es_ES


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