Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks
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...
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doaj-dc9dd9307a2b4187a34d4a99f569f5aa2021-04-26T23:01:36ZengMDPI AGSensors1424-82202021-04-01213030303010.3390/s21093030Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor NetworksAce Dimitrievski0Eftim Zdravevski1Petre Lameski2María Vanessa Villasana3Ivan Miguel Pires4Nuno M. Garcia5Francisco Flórez-Revuelta6Vladimir Trajkovik7Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, 1000 Skopje, MacedoniaFaculty of Computer Science and Engineering, Ss. Cyril and Methodius University, 1000 Skopje, MacedoniaFaculty of Computer Science and Engineering, Ss. Cyril and Methodius University, 1000 Skopje, MacedoniaFaculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilhã, PortugalInstituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, PortugalInstituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, PortugalDepartment of Computer Technology, Universidad de Alicante, 03690 Alicante, SpainFaculty of Computer Science and Engineering, Ss. Cyril and Methodius University, 1000 Skopje, MacedoniaPneumonia 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.https://www.mdpi.com/1424-8220/21/9/3030COVID-19sensorsconnected healthcare |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ace Dimitrievski Eftim Zdravevski Petre Lameski María Vanessa Villasana Ivan Miguel Pires Nuno M. Garcia Francisco Flórez-Revuelta Vladimir Trajkovik |
spellingShingle |
Ace Dimitrievski Eftim Zdravevski Petre Lameski María Vanessa Villasana Ivan Miguel Pires Nuno M. Garcia Francisco Flórez-Revuelta Vladimir Trajkovik Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks Sensors COVID-19 sensors connected healthcare |
author_facet |
Ace Dimitrievski Eftim Zdravevski Petre Lameski María Vanessa Villasana Ivan Miguel Pires Nuno M. Garcia Francisco Flórez-Revuelta Vladimir Trajkovik |
author_sort |
Ace Dimitrievski |
title |
Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks |
title_short |
Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks |
title_full |
Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks |
title_fullStr |
Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks |
title_full_unstemmed |
Towards Detecting Pneumonia Progression in COVID-19 Patients by Monitoring Sleep Disturbance Using Data Streams of Non-Invasive Sensor Networks |
title_sort |
towards detecting pneumonia progression in covid-19 patients by monitoring sleep disturbance using data streams of non-invasive sensor networks |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-04-01 |
description |
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. |
topic |
COVID-19 sensors connected healthcare |
url |
https://www.mdpi.com/1424-8220/21/9/3030 |
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