potential diagnostic tools for COVID-19 auscultation
Vimal Raj (Author), A. Renjini (Author), Mohanachandran Nair Sindhu Swapna (Author), S. Sreejyothi (Author), Sankaranarayana Iyer Sankararaman (Author)

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;

Data

Language: English
Year of publishing:
Typology: 1.01 - Original Scientific Article
Organization: UNG - University of Nova Gorica
UDC: 53
COBISS: 112999171 Link will open in a new window
ISSN: 0960-0779
Views: 538
Downloads: 0
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Other data

URN: URN:SI:UNG
Type (COBISS): Not categorized
Pages: str. 1-8
Issue: ǂVol. ǂ140
Chronology: 2020
DOI: 10.1016/j.chaos.2020.110246
ID: 15766874