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Naslov:Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation
Avtorji:ID Swapna, Mohanachandran Nair Sindhu, UNIVERSITY OF KERALA (Avtor)
ID VIMAL, RAJ, UNIVERSITY OF KERALA (Avtor)
ID A, RENJINI, UNIVERSITY OF KERALA (Avtor)
ID S, SREEJYOTHI, UNIVERSITY OF KERALA (Avtor)
ID S, SANKARARMAN, UNIVERSITY OF KERALA (Avtor)
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:The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.
Ključne besede:Breath sound analysis, Fractal dimension, Nonlinear time series analysis, Sample entropy, Hurst exponent, Principal component analysis
Verzija publikacije:Objavljena publikacija
Leto izida:2020
Št. strani:8
Številčenje:11, 140
PID:20.500.12556/RUNG-7396 Novo okno
COBISS.SI-ID:112999171 Novo okno
DOI:10.1016/j.chaos.2020.110246 Novo okno
NUK URN:URN:SI:UNG:REP:77BPWAEO
Datum objave v RUNG:28.06.2022
Število ogledov:3008
Število prenosov:0
Metapodatki:XML DC-XML DC-RDF
:
SWAPNA, Mohanachandran Nair Sindhu, VIMAL, RAJ, A, RENJINI, S, SREEJYOTHI in S, SANKARARMAN, 2020, Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation. Chaos, Solitons & Fractals [na spletu]. 2020. Vol. 11, no. 140. [Dostopano 8 april 2025]. DOI 10.1016/j.chaos.2020.110246. Pridobljeno s: https://repozitorij.ung.si/IzpisGradiva.php?lang=slv&id=7396
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Gradivo je del revije

Naslov:Chaos, Solitons & Fractals
Založnik:ELSEVIER
Leto izida:2020
ISSN:0960-0779

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:28.06.2022

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