| Title: | Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation |
|---|
| Authors: | ID Swapna, Mohanachandran Nair Sindhu, UNIVERSITY OF KERALA (Author)
ID VIMAL, RAJ, UNIVERSITY OF KERALA (Author)
ID A, RENJINI, UNIVERSITY OF KERALA (Author)
ID S, SREEJYOTHI, UNIVERSITY OF KERALA (Author)
ID S, SANKARARMAN, UNIVERSITY OF KERALA (Author) |
|---|
| Files: |
This document has no files that are freely available to the public. This document may have a physical copy in the library of the organization, check the status via COBISS.  |
|---|
| Language: | English |
|---|
| Work type: | Not categorized |
|---|
| Typology: | 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 |
|---|
| Publication version: | Version of Record |
|---|
| Year of publishing: | 2020 |
|---|
| Number of pages: | 8 |
|---|
| Numbering: | 11, 140 |
|---|
| PID: | 20.500.12556/RUNG-7396 |
|---|
| COBISS.SI-ID: | 112999171 |
|---|
| DOI: | 10.1016/j.chaos.2020.110246 |
|---|
| NUK URN: | URN:SI:UNG:REP:77BPWAEO |
|---|
| Publication date in RUNG: | 28.06.2022 |
|---|
| Views: | 2502 |
|---|
| Downloads: | 0 |
|---|
| Metadata: |    |
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
