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Sensors
Volume 21
Issue 9
10.3390/s21093030
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Communication

Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks

by
Ace Dimitrievski
1,*,
Eftim Zdravevski
1,
Petre Lameski
1,
María Vanessa Villasana
2,
Ivan Miguel Pires
3,4,5,
Nuno M. Garcia
3,
Francisco Flórez-Revuelta
6 and
Vladimir Trajkovik
1
1
Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, 1000 Skopje, Macedonia
2
Faculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilhã, Portugal
3
Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
4
Computer Science Department, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
5
UICISA:E Research Centre, School of Health, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
6
Department of Computer Technology, Universidad de Alicante, 03690 Alicante, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Gianvito Pio
Sensors 2021, 21(9), 3030; https://doi.org/10.3390/s21093030
Received: 16 February 2021 / Revised: 8 April 2021 / Accepted: 9 April 2021 / Published: 26 April 2021
(This article belongs to the Special Issue Geo-Distributed Big Data Analytics in Sensor Networks)
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Abstract

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. View Full-Text
Keywords: COVID-19; sensors; connected healthcare COVID-19; sensors; connected healthcare
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MDPI and ACS Style

Dimitrievski, A.; Zdravevski, E.; Lameski, P.; Villasana, M.V.; Miguel Pires, I.; Garcia, N.M.; 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, 3030. https://doi.org/10.3390/s21093030

AMA Style

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

Chicago/Turabian Style

Dimitrievski, Ace, Eftim Zdravevski, Petre Lameski, María V. Villasana, Ivan Miguel Pires, Nuno M. Garcia, Francisco Flórez-Revuelta, and Vladimir Trajkovik. 2021. "Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks" Sensors 21, no. 9: 3030. https://doi.org/10.3390/s21093030

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