Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks
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Título: | Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks |
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Autor/es: | Dimitrievski, Ace | Zdravevski, Eftim | Lameski, Petre | Villasana, María Vanessa | Pires, Ivan Miguel | Garcia, Nuno M. | Flórez-Revuelta, Francisco | Trajkovik, Vladimir |
Grupo/s de investigación o GITE: | Informática Industrial y Redes de Computadores |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | COVID-19 | Sensors | Connected healthcare |
Área/s de conocimiento: | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | 26-abr-2021 |
Editor: | MDPI |
Cita bibliográfica: | Dimitrievski A, Zdravevski E, Lameski P, Villasana MV, Miguel Pires I, Garcia NM, Flórez-Revuelta F, Trajkovik V. Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks. Sensors. 2021; 21(9):3030. https://doi.org/10.3390/s21093030 |
Resumen: | Pneumonia caused by COVID-19 is a severe health risk that sometimes leads to fatal outcomes. Due to constraints in medical care systems, technological solutions should be applied to diagnose, monitor, and alert about the disease’s progress for patients receiving care at home. Some sleep disturbances, such as obstructive sleep apnea syndrome, can increase the risk for COVID-19 patients. This paper proposes an approach to evaluating patients’ sleep quality with the aim of detecting sleep disturbances caused by pneumonia and other COVID-19-related pathologies. We describe a non-invasive sensor network that is used for sleep monitoring and evaluate the feasibility of an approach for training a machine learning model to detect possible COVID-19-related sleep disturbances. We also discuss a cloud-based approach for the implementation of the proposed system for processing the data streams. Based on the preliminary results, we conclude that sleep disturbances are detectable with affordable and non-invasive sensors. |
Patrocinador/es: | A.D., E.Z., P.L., and V.T. acknowledge the partial funding by the Ss. Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering. This work was also partially funded by FCT/MEC through national funds and co-funded by FEDER—PT2020 partnership agreement under the project UIDB/50008/2020 (Este trabalho é parcialmente financiado pela FCT/MEC através de fundos nacionais e cofinanciado pelo FEDER, no âmbito do Acordo de Parceria PT2020 no âmbito do projeto UIDB/50008/2020). This work was also partially funded by National Funds through the FCT—Foundation for Science and Technology, I.P., within the scope of the project UIDB/00742/2020. Furthermore, I.P. would like to thank the Politécnico de Viseu for their support. This article is based upon work from Sheldon COST Action CA16226 Indoor Living Space Improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). COST is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. www.cost.eu (accessed on 1 April 2021). |
URI: | http://hdl.handle.net/10045/115458 |
ISSN: | 1424-8220 |
DOI: | 10.3390/s21093030 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/s21093030 |
Aparece en las colecciones: | INV - I2RC - Artículos de Revistas INV - AmI4AHA - Artículos de Revistas |
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