Repozitorij Univerze v Novi Gorici

Izpis gradiva
A+ | A- | SLO | ENG

Naslov:Bioacoustic signal analysis through complex network features
Avtorji:Mohanachandran Nair Sindhu, Swapna (Avtor)
VIMAL, RAJ (Avtor)
S, Sankararaman (Avtor)
Datoteke:Gradivo nima datotek. 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 (r6)
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
Leto izida:2022
Št. strani:8
Številčenje:145, 6
COBISS_ID:113350147  Povezava se odpre v novem oknu
URN:URN:SI:UNG:REP:PERBKSNM
DOI:10.1016/j.compbiomed.2022.105491 Povezava se odpre v novem oknu
Licenca:CC BY-NC-ND 4.0
To delo je dosegljivo pod licenco Creative Commons Priznanje avtorstva-Nekomercialno-Brez predelav 4.0 Mednarodna
Število ogledov:377
Število prenosov:0
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
Področja:Gradivo ni uvrščeno v področja.
:
  
Skupna ocena:(0 glasov)
Vaša ocena:Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.

Postavite miškin kazalec na naslov za izpis povzetka. Klik na naslov izpiše podrobnosti ali sproži prenos.

Gradivo je del revije

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

Nazaj