Repository of University of Nova Gorica

Show document
A+ | A- | Help | SLO | ENG

Title:Neural net pattern recognition based auscultation of croup cough and pertussis using phase portrait features
Authors:ID Swapna, Mohanachandran Nair Sindhu, UNIVERSITY OF KERALA (Author), et al.
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. Link is opened in a new window
Language:English
Work type:Not categorized
Typology:1.01 - Original Scientific Article
Organization:UNG - University of Nova Gorica
Abstract:Cough signal analysis for understanding the pathological condition has become important from the outset of the exigency posed by the epidemic COVID-19. The present work suggests a surrogate approach for the classification of cough signals - croup cough (CC) and pertussis (PT) – based on spectral, fractal, and nonlinear time-series techniques. The spectral analysis of CC reveals the presence of more frequency components in the short duration cough sound compared to PT. The musical nature of CC is unveiled not only through the spectral analysis but also through the phase portrait features – sample entropy (S), maximal Lyapunov exponent (L), and Hurst exponent (Hb). The modifications in the internal morphology of the respiratory tract, giving rise to more frequency components associated with the complex airflow dynamics, get staged through the higher fractal dimension of CC. Among the two supervised classification tools, cubic KNN (CKNN) and neural net pattern recognition (NNPR), used for classifying the CC and PT signals based on nonlinear time series parameters, NNPR is found better. Thus, the study opens the possibility of identification of pulmonary pathological conditions through cough sound signal analysis.
Keywords:Croup cough Pertussis Fractal dimension Phase portrait Sample entropy Machine learning techniques
Year of publishing:2021
Number of pages:214-222
Numbering:4, 72
PID:20.500.12556/RUNG-7458 New window
COBISS.SI-ID:113746947 New window
DOI:https://doi.org/10.1016/j.cjph.2021.05.002 New window
NUK URN:URN:SI:UNG:REP:GHXYAVBV
Publication date in RUNG:04.07.2022
Views:1197
Downloads:0
Metadata:XML RDF-CHPDL DC-XML DC-RDF
:
Copy citation
  
Average score:(0 votes)
Your score:Voting is allowed only for logged in users.
Share:Bookmark and Share


Hover the mouse pointer over a document title to show the abstract or click on the title to get all document metadata.

Record is a part of a journal

Title:Chinese Journal of Physics
Year of publishing:2021

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:04.07.2022

Back