Time series and fractal analyses of wheezing

ǂa ǂ novel approach

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

Abstract

Since the outbreak of the pandemic Coronavirus Disease 2019, the world is in search of novel non-invasive methods for safer and early detection of lung diseases. The pulmonary pathological symptoms refected through the lung sound opens a possibility of detection through auscultation and of employing spectral, fractal, nonlinear time series and principal component analyses. Thirty-fve signals of vesicular and expiratory wheezing breath sound, subjected to spectral analyses shows a clear distinction in terms of time duration, intensity, and the number of frequency components. An investigation of the dynamics of air molecules during respiration using phase portrait, Lyapunov exponent, sample entropy, fractal dimension, and Hurst exponent helps in understanding the degree of complexity arising due to the presence of mucus secretions and constrictions in the respiratory airways. The feature extraction of the power spectral density data and the application of principal component analysis helps in distinguishing vesicular and expiratory wheezing and thereby, giving a ray of hope in accomplishing an early detection of pulmonary diseases through sound signal analysis.

Keywords

auscultation;wheeze;fractals;nonlinear time series analysis;sample entropy;

Data

Language:	English
Year of publishing:	2020
Typology:	1.01 - Original Scientific Article
Organization:	UNG - University of Nova Gorica
UDC:	53
COBISS:	113308675
ISSN:	2662-4729
Views:	511
Downloads:	0
Average score:	0 (0 votes)
Metadata:

Other data

URN:	URN:SI:UNG
Pages:	str. 1339–1347
Volume:	ǂVol. ǂ43
Issue:	ǂiss. ǂ4
Chronology:	2020
DOI:	10.1007/s13246-020-00937-5
ID:	15786685