Repozitorij Univerze v Novi Gorici

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Naslov:Bioacoustic signal analysis through complex network features
Avtorji:ID Mohanachandran Nair Sindhu, Swapna, UNIVERSITY OF KERALA (Avtor)
ID VIMAL, RAJ, UNIVERSITY OF KERALA (Avtor)
ID S, Sankararaman, 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 paper proposes a graph-theoretical approach to auscultation, bringing out the potential of graph features in classifying the bioacoustics signals. The complex network analysis of the bioacoustics signals - vesicular (VE) and bronchial (BR) breath sound - of 48 healthy persons are carried out for understanding the airflow dynamics during respiration. The VE and BR are classified by the machine learning techniques extracting the graph features – the number of edges (E), graph density (D), transitivity (T), degree centrality (Dcg) and eigenvector centrality (Ecg). The higher value of E, D, and T in BR indicates the temporally correlated airflow through the wider tracheobronchial tract resulting in sustained high-intense low-frequencies. The frequency spread and high-frequencies in VE, arising due to the less correlated airflow through the narrow segmental bronchi and lobar, appears as a lower value for E, D, and T. The lower values of Dcg and Ecg justify the inferences from the spectral and other graph parameters. The study proposes a methodology in remote auscultation that can be employed in the current scenario of COVID-19.
Ključne besede:Bioacoustic signal, Graph theory, Complex network, Lung auscultation
Verzija članka:Založnikova različica članka
Leto izida:2022
Št. strani:8
Številčenje:6, 145
PID:20.500.12556/RUNG-7441 Novo okno
COBISS.SI-ID:113350147 Novo okno
DOI:10.1016/j.compbiomed.2022.105491 Novo okno
NUK URN:URN:SI:UNG:REP:PERBKSNM
Datum objave v RUNG:30.06.2022
Število ogledov:721
Število prenosov:0
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Skupna ocena:(0 glasov)
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Gradivo je del revije

Naslov:Computers in Biology and Medicine
Založnik:ELSEVIER
Leto izida:2022
ISSN:18790534

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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