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Title:Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation
Authors:MOHANACHANDRAN NAIR SINDHU, SWAPNA (Author)
VIMAL, RAJ (Author)
A, RENJINI (Author)
S, SREEJYOTHI (Author)
S, SANKARARMAN (Author)
Files:This document has no files. This document may have a phisical copy in the library of the organization, check the status via COBISS. Link is opened in a new window
Language:English
Work type:Not categorized (r6)
Tipology:1.01 - Original Scientific Article
Organization:UNG - University of Nova Gorica
Abstract: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.
Keywords:Breath sound analysis, Fractal dimension, Nonlinear time series analysis, Sample entropy, Hurst exponent, Principal component analysis
Year of publishing:2020
Number of pages:8
Numbering:140, 11
COBISS_ID:112999171 Link is opened in a new window
URN:URN:SI:UNG:REP:77BPWAEO
DOI:10.1016/j.chaos.2020.110246 Link is opened in a new window
License:CC BY-NC-ND 4.0
This work is available under this license: Creative Commons Attribution Non-Commercial No Derivatives 4.0 International
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Record is a part of a journal

Title:Chaos, Solitons & Fractals
Publisher:ELSEVIER
ISSN:0960-0779
Year of publishing:2020

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