Nonlinear time series and principal component analyses

potential diagnostic tools for COVID-19 auscultation

Vimal Raj (Avtor), A. Renjini (Avtor), Mohanachandran Nair Sindhu Swapna (Avtor), S. Sreejyothi (Avtor), Sankaranarayana Iyer Sankararaman (Avtor)

Povzetek

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.

Ključne besede

breath sound analysis;fractal dimension;nonlinear time series analysis;sample entropy;hurst exponent;principal component analysis;

Podatki

Jezik:	Angleški jezik
Leto izida:	2020
Tipologija:	1.01 - Izvirni znanstveni članek
Organizacija:	UNG - Univerza v Novi Gorici
UDK:	53
COBISS:	112999171
ISSN:	0960-0779
Št. ogledov:	538
Št. prenosov:	0
Ocena:	0 (0 glasov)
Metapodatki:

Ostali podatki

URN:	URN:SI:UNG
Vrsta dela (COBISS):	Delo ni kategorizirano
Strani:	str. 1-8
Zvezek:	ǂVol. ǂ140
Čas izdaje:	2020
DOI:	10.1016/j.chaos.2020.110246
ID:	15766874