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Naslov:Phase Portrait for High Fidelity Feature Extraction and Classification: A Surrogate 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 paper proposes a novel surrogate method of classification of breath sound signals for auscultation through the principal component analysis (PCA), extracting the features of a phase portrait. The nonlinear parameters of the phase portrait like the Lyapunov exponent, the sample entropy, the fractal dimension, and the Hurst exponent help in understanding the degree of complexity arising due to the turbulence of air molecules in the airways of the lungs. Thirty-nine breath sound signals of bronchial breath (BB) and pleural rub (PR) are studied through spectral, fractal, and phase portrait analyses. The fast Fourier transform and wavelet analyses show a lesser number of high-intense, low-frequency components in PR, unlike BB. The fractal dimension and sample entropy values for PR are, respectively, 1.772 and 1.041, while those for BB are 1.801 and 1.331, respectively. This study reveals that the BB signal is more complex and random, as evidenced by the fractal dimension and sample entropy values. The signals are classified by PCA based on the features extracted from the power spectral density (PSD) data and the features of the phase portrait. The PCA based on the features of the phase portrait considers the temporal correlation of the signal amplitudes and that based on the PSD data considers only the signal amplitudes, suggesting that the former method is better than the latter as it reflects the multidimensional aspects of the signal. This appears in the PCA-based classification as 89.6% for BB, a higher variance than the 80.5% for the PR signal, suggesting the higher fidelity of the phase portrait-based classification.
Ključne besede:Phase Portrait, time series, feature extraction, pleural rub
Leto izida:2020
Št. strani:8
Številčenje:11, 30
PID:20.500.12556/RUNG-7467 Novo okno
COBISS.SI-ID:113794563 Novo okno
DOI:10.1063/5.0020121 Novo okno
NUK URN:URN:SI:UNG:REP:WEQLRFHT
Datum objave v RUNG:05.07.2022
Število ogledov:2364
Število prenosov:0
Metapodatki:XML DC-XML DC-RDF
:
SWAPNA, Mohanachandran Nair Sindhu, 2020, Phase Portrait for High Fidelity Feature Extraction and Classification: A Surrogate Approach. Chaos: An Interdisciplinary Journal of Nonlinear Science [na spletu]. 2020. Vol. 11, no. 30. [Dostopano 8 april 2025]. DOI 10.1063/5.0020121. Pridobljeno s: https://repozitorij.ung.si/IzpisGradiva.php?lang=slv&id=7467
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Gradivo je del revije

Naslov:Chaos: An Interdisciplinary Journal of Nonlinear Science
Leto izida:2020

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:04.07.2022

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