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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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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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/194328

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Title: IoT and fog computing-based monitoring system for cardiovascular patients with automatic ECG classification using deep neural networks
Author: Rincón-Arango, Jaime Andrés Guerra-Ojeda, Solanye Carrascosa Casamayor, Carlos Julian, Vicente
UPV Unit: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Issued date:
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 ...[+]
Subjects: Cardiovascular Diseases , ECG , IoT , Fog-AI , LoRa , Edge-AI
Copyrigths: Reconocimiento (by)
Source:
Sensors. (eissn: 1424-8220 )
DOI: 10.3390/s20247353
Publisher:
MDPI AG
Publisher version: https://doi.org/10.3390/s20247353
Project ID:
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/
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/
info:eu-repo/grantAgreement/COLCIENCIAS//COLDOC 679/
Thanks:
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 ...[+]
Type: Artículo

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