Cargando…

Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron

Contactless or non-invasive technology has a significant impact on healthcare applications such as the prediction of COVID-19 symptoms. Non-invasive methods are essential especially during the COVID-19 pandemic as they minimise the burden on healthcare personnel. One notable symptom of COVID-19 infe...

Descripción completa

Detalles Bibliográficos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IEEE 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8768992/
https://www.ncbi.nlm.nih.gov/pubmed/35790093
http://dx.doi.org/10.1109/JSEN.2021.3096641
_version_ 1784635032494669824
collection PubMed
description Contactless or non-invasive technology has a significant impact on healthcare applications such as the prediction of COVID-19 symptoms. Non-invasive methods are essential especially during the COVID-19 pandemic as they minimise the burden on healthcare personnel. One notable symptom of COVID-19 infection is a rapid respiratory rate, which requires constant real-time monitoring of respiratory patterns. In this paper, Software Defined Radio (SDR) based Radio-Frequency sensing technique and supervised machine learning algorithm is employed to provide a platform for detecting and monitoring various respiratory: eupnea, biot, bradypnea, sighing, tachypnea, and kussmaul. The variations in Channel State Information produced by human respiratory were utilised to identify distinct respiratory patterns using fine-grained Orthogonal Frequency-Division Multiplexing signals. The proposed platform based on the SDR and the Deep Multilayer Perceptron classifier exhibits the ability to effectively detect and classify the afore-mentioned distinct respiratory with an accuracy of up to 99%. Moreover, the effectiveness of the proposed scheme in terms of diagnosis accuracy, precision, recall, F1-score, and confusion matrix is demonstrated by comparison with a state-of-the-art machine learning classifier: Random Forest.
format Online
Article
Text
id pubmed-8768992
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher IEEE
record_format MEDLINE/PubMed
spelling pubmed-87689922022-06-29 Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron IEEE Sens J Article Contactless or non-invasive technology has a significant impact on healthcare applications such as the prediction of COVID-19 symptoms. Non-invasive methods are essential especially during the COVID-19 pandemic as they minimise the burden on healthcare personnel. One notable symptom of COVID-19 infection is a rapid respiratory rate, which requires constant real-time monitoring of respiratory patterns. In this paper, Software Defined Radio (SDR) based Radio-Frequency sensing technique and supervised machine learning algorithm is employed to provide a platform for detecting and monitoring various respiratory: eupnea, biot, bradypnea, sighing, tachypnea, and kussmaul. The variations in Channel State Information produced by human respiratory were utilised to identify distinct respiratory patterns using fine-grained Orthogonal Frequency-Division Multiplexing signals. The proposed platform based on the SDR and the Deep Multilayer Perceptron classifier exhibits the ability to effectively detect and classify the afore-mentioned distinct respiratory with an accuracy of up to 99%. Moreover, the effectiveness of the proposed scheme in terms of diagnosis accuracy, precision, recall, F1-score, and confusion matrix is demonstrated by comparison with a state-of-the-art machine learning classifier: Random Forest. IEEE 2021-07-12 /pmc/articles/PMC8768992/ /pubmed/35790093 http://dx.doi.org/10.1109/JSEN.2021.3096641 Text en This article is free to access and download, along with rights for full text and data mining, re-use and analysis.
spellingShingle Article
Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron
title Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron
title_full Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron
title_fullStr Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron
title_full_unstemmed Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron
title_short Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron
title_sort wireless channel modelling for identifying six types of respiratory patterns with sdr sensing and deep multilayer perceptron
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8768992/
https://www.ncbi.nlm.nih.gov/pubmed/35790093
http://dx.doi.org/10.1109/JSEN.2021.3096641
work_keys_str_mv AT wirelesschannelmodellingforidentifyingsixtypesofrespiratorypatternswithsdrsensinganddeepmultilayerperceptron
AT wirelesschannelmodellingforidentifyingsixtypesofrespiratorypatternswithsdrsensinganddeepmultilayerperceptron
AT wirelesschannelmodellingforidentifyingsixtypesofrespiratorypatternswithsdrsensinganddeepmultilayerperceptron
AT wirelesschannelmodellingforidentifyingsixtypesofrespiratorypatternswithsdrsensinganddeepmultilayerperceptron
AT wirelesschannelmodellingforidentifyingsixtypesofrespiratorypatternswithsdrsensinganddeepmultilayerperceptron
AT wirelesschannelmodellingforidentifyingsixtypesofrespiratorypatternswithsdrsensinganddeepmultilayerperceptron
AT wirelesschannelmodellingforidentifyingsixtypesofrespiratorypatternswithsdrsensinganddeepmultilayerperceptron