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Graph based feature extraction and classification of wet and dry cough signals: A machine learning approachMohanachandran Nair Sindhu Swapna, 2021, original scientific article
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
Published in RUNG: 30.06.2022; Views: 1058; Downloads: 0
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