Repozitorij Univerze v Novi Gorici

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Naslov:Neural net pattern recognition based auscultation of croup cough and pertussis using phase portrait features
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:Cough signal analysis for understanding the pathological condition has become important from the outset of the exigency posed by the epidemic COVID-19. The present work suggests a surrogate approach for the classification of cough signals - croup cough (CC) and pertussis (PT) – based on spectral, fractal, and nonlinear time-series techniques. The spectral analysis of CC reveals the presence of more frequency components in the short duration cough sound compared to PT. The musical nature of CC is unveiled not only through the spectral analysis but also through the phase portrait features – sample entropy (S), maximal Lyapunov exponent (L), and Hurst exponent (Hb). The modifications in the internal morphology of the respiratory tract, giving rise to more frequency components associated with the complex airflow dynamics, get staged through the higher fractal dimension of CC. Among the two supervised classification tools, cubic KNN (CKNN) and neural net pattern recognition (NNPR), used for classifying the CC and PT signals based on nonlinear time series parameters, NNPR is found better. Thus, the study opens the possibility of identification of pulmonary pathological conditions through cough sound signal analysis.
Ključne besede:Croup cough Pertussis Fractal dimension Phase portrait Sample entropy Machine learning techniques
Leto izida:2021
Št. strani:214-222
Številčenje:4, 72
PID:20.500.12556/RUNG-7458 Novo okno
COBISS.SI-ID:113746947 Novo okno
DOI:https://doi.org/10.1016/j.cjph.2021.05.002 Novo okno
NUK URN:URN:SI:UNG:REP:GHXYAVBV
Datum objave v RUNG:04.07.2022
Število ogledov:1313
Število prenosov:0
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Gradivo je del revije

Naslov:Chinese Journal of Physics
Leto izida:2021

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
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Začetek licenciranja:04.07.2022

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