Repozitorij Univerze v Novi Gorici

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Naslov:Graph based feature extraction and classification of wet and dry cough signals: A machine learning approach
Avtorji:ID Swapna, Mohanachandran Nair Sindhu, UNIVERSITY OF KERALA (Avtor), et al.
Datoteke: Gradivo nima datotek, ki so prostodostopne za javnost. Gradivo je morda fizično dosegljivo v knjižnici fakultete, zalogo lahko preverite v COBISS-u. Povezava se odpre v novem oknu
Jezik:Angleški jezik
Vrsta gradiva:Delo ni kategorizirano
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:UNG - Univerza v Novi Gorici
Opis:This article proposes a unique approach to bring out the potential of graph-based features to reveal the hidden signatures of wet (WE) and dry (DE) cough signals, which are the suggestive symptoms of various respiratory ailments like COVID 19. The spectral and complex network analyses of 115 cough signals are employed for perceiving the airflow dynamics through the infected respiratory tract while coughing. The different phases of WE and DE are observed from their time-domain signals, indicating the operation of the glottis. The wavelet analysis of WE shows a frequency spread due to the turbulence in the respiratory tract. The complex network features namely degree centrality, eigenvector centrality, transitivity, graph density and graph entropy not only distinguish WE and DE but also reveal the associated airflow dynamics. A better distinguishability between WE and DE is obtained through the supervised machine learning techniques (MLTs)—quadratic support vector machine and neural net pattern recognition (NN), when compared to the unsupervised MLT, principal component analysis. The 93.90% classification accuracy with a precision of 97.00% suggests NN as a better classifier using complex network features. The study opens up the possibility of complex network analysis in remote auscultation.
Ključne besede:wet cough, dry cough, complex network, quadratic SVM, neural net
Verzija publikacije:Objavljena publikacija
Leto izida:2021
Št. strani:11
Številčenje:6, 9
PID:20.500.12556/RUNG-7445 Novo okno
COBISS.SI-ID:113433859 Novo okno
DOI:10.1093/comnet/cnab039 Novo okno
NUK URN:URN:SI:UNG:REP:KGZDDAPU
Datum objave v RUNG:30.06.2022
Število ogledov:1212
Število prenosov:0
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Gradivo je del revije

Naslov:Journal of Complex networks
Leto izida:2021
ISSN:20511329

Licence

Licenca:CC BY-NC-ND 4.0, Creative Commons Priznanje avtorstva-Nekomercialno-Brez predelav 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by-nc-nd/4.0/deed.sl
Opis:Najbolj omejujoča licenca Creative Commons. Uporabniki lahko prenesejo in delijo delo v nekomercialne namene in ga ne smejo uporabiti za nobene druge namene.
Začetek licenciranja:30.06.2022

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