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Title:Graph based feature extraction and classification of wet and dry cough signals: A machine learning approach
Authors:ID Swapna, Mohanachandran Nair Sindhu, UNIVERSITY OF KERALA (Author), et al.
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Language:English
Work type:Not categorized
Typology:1.01 - Original Scientific Article
Organization:UNG - University of Nova Gorica
Abstract:This article proposes a unique approach to bring out the potential of graph-based features to reveal the hidden signatures of wet (WE) and dry (DE) cough signals, which are the suggestive symptoms of various respiratory ailments like COVID 19. The spectral and complex network analyses of 115 cough signals are employed for perceiving the airflow dynamics through the infected respiratory tract while coughing. The different phases of WE and DE are observed from their time-domain signals, indicating the operation of the glottis. The wavelet analysis of WE shows a frequency spread due to the turbulence in the respiratory tract. The complex network features namely degree centrality, eigenvector centrality, transitivity, graph density and graph entropy not only distinguish WE and DE but also reveal the associated airflow dynamics. A better distinguishability between WE and DE is obtained through the supervised machine learning techniques (MLTs)—quadratic support vector machine and neural net pattern recognition (NN), when compared to the unsupervised MLT, principal component analysis. The 93.90% classification accuracy with a precision of 97.00% suggests NN as a better classifier using complex network features. The study opens up the possibility of complex network analysis in remote auscultation.
Keywords:wet cough, dry cough, complex network, quadratic SVM, neural net
Publication version:Version of Record
Year of publishing:2021
Number of pages:11
Numbering:6, 9
PID:20.500.12556/RUNG-7445 New window
COBISS.SI-ID:113433859 New window
DOI:10.1093/comnet/cnab039 New window
NUK URN:URN:SI:UNG:REP:KGZDDAPU
Publication date in RUNG:30.06.2022
Views:1326
Downloads:0
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Record is a part of a journal

Title:Journal of Complex networks
Year of publishing:2021
ISSN:20511329

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:30.06.2022

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