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 |