Thoracal motion-based analysis of breathing patterns in individuals with a mild-moderate Covid-19 history using machine learning techniques: A single blinded multidisciplinary study on post-Covid

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dc.authoridKurt, İlke/0000-0001-5911-9282
dc.authoridSELÇUK, Halit/0000-0003-2760-4130
dc.authoridUlukaya, Sezer/0000-0003-0473-7547
dc.authorwosidKurt, İlke/AAG-6476-2019
dc.authorwosidSELÇUK, Halit/P-5348-2018
dc.authorwosidUlukaya, Sezer/N-9772-2015
dc.contributor.authorKurt, Lke
dc.contributor.authorSelcuk, Halit
dc.contributor.authorUlukaya, Sezer
dc.contributor.authorOzturk, Guelnur
dc.contributor.authorKeklicek, Hilal
dc.date.accessioned2024-06-12T10:58:50Z
dc.date.available2024-06-12T10:58:50Z
dc.date.issued2024
dc.departmentTrakya Üniversitesien_US
dc.description.abstractBackground: Covid-19 led to deaths worldwide and left significant sequelae in a lot of people. Thoracic movements are important for the proper functioning of the respiratory system. However, there is no study on how the thoracic mobility of individuals who have recovered fully from Covid-19 is affected. Methods: In this study, the differences between thorax movements of healthy individuals and individuals with Covid-19 were investigated from a multidisciplinary perspective for the first time. Spontaneous and deep breathing data under two (at sitting- at standing) different conditions were collected and analyzed. In terms of engineering, using the Boruta feature selection method and various machine learning algorithms, discriminative features that will benefit clinically were determined. Clinically, the effect of Covid-19 was examined statistically in terms of respiratory biomechanics with thoracal motion-based analysis of 22 individuals. Results: The use of Boruta in sitting and standing positions during deep breathing increased the classification performance. In spontaneous breathing, using Boruta only in the sitting position provided an increase in classification performance achieving an accuracy of 95.45 %. The results of the study showed that respiratory movements of the thoracic cage in the anteroposterior and craniocaudal directions were more restricted and had weaker respiratory acceleration skills in individuals with a Covid-19 history (p < 0.05). Conclusion: From a clinical point of view, it was observed that the respiratory acceleration movements were restricted in individuals with a Covid-19 history even though full recovery. Also, it was revealed that machine learning models can classify with high performance in situations requiring effort.en_US
dc.identifier.doi10.1016/j.bspc.2023.105429
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85172401372en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2023.105429
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20194
dc.identifier.volume87en_US
dc.identifier.wosWOS:001084000100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing And Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectCovid-19en_US
dc.subjectMachine Learningen_US
dc.subjectPost-Coviden_US
dc.subjectPulmonary Rehabilitationen_US
dc.subjectRespiratory Patternen_US
dc.titleThoracal motion-based analysis of breathing patterns in individuals with a mild-moderate Covid-19 history using machine learning techniques: A single blinded multidisciplinary study on post-Coviden_US
dc.typeArticleen_US

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