1. Time series and mel frequency analyses of wet and dry cough signals : a neural net classificationAmmini Renjini, Mohanachandran Nair Sindhu Swapna, K. Satheesh Kumar, Sankaranarayana Iyer Sankararaman, 2023, izvirni znanstveni članek Ključne besede: time series, mel frequency, cough signal, wet cough, dry cough, phase portrait, mel coefficients, fractal dimension, neural network
Objavljeno v RUNG: 29.09.2023; Ogledov: 1602; Prenosov: 6
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2. Neural net pattern recognition based auscultation of croup cough and pertussis using phase portrait featuresMohanachandran Nair Sindhu Swapna, 2021, izvirni znanstveni članek Opis: 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.
Ključne besede: Croup cough
Pertussis
Fractal dimension
Phase portrait
Sample entropy
Machine learning techniques
Objavljeno v RUNG: 04.07.2022; Ogledov: 1840; Prenosov: 0
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3. Graph based feature extraction and classification of wet and dry cough signals: A machine learning approachMohanachandran Nair Sindhu Swapna, 2021, izvirni znanstveni članek Opis: 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.
Ključne besede: wet cough, dry cough, complex network, quadratic SVM, neural net
Objavljeno v RUNG: 30.06.2022; Ogledov: 1809; Prenosov: 0
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4. Complex network-based cough signal analysis for digital auscultation: a machine learning approachMohanachandran Nair Sindhu Swapna, 2022, izvirni znanstveni članek Opis: The paper proposes a novel approach to bring out the potential of complex networks based on graph theory to unwrap the hidden characteristics of cough signals, croup (BC), and pertussis (PS). The spectral and complex network analyses of 48 cough sounds are utilized for understanding the airflow through the infected respiratory tract. Among the different phases of the cough sound time-domain signals of BC and PS – expulsive (X), intermediate (I), and voiced (V) - the phase ‘I’ is noisy in BC due to improper glottal functioning. The spectral analyses reveal high-frequency components in both cough signals with an additional high-intense low-frequency spread in BC. The complex network features created by the correlation mapping approach, like number of edges (E), graph density (G), transitivity (), degree centrality (D), average path length (L), and number of components () distinguishes BC and PS. The higher values of E, G, and for BC indicate its musical nature through the strong correlation between the signal segments and the presence of high-intense low-frequency components in BC, unlike that in PS. The values of D, L, and discriminate BC and PS in terms of the strength of the correlation between the nodes within them. The linear discriminant analysis (LDA) and quadratic support vector machine (QSVM) classifies BC and PS, with greater accuracy of 94.11% for LDA. The proposed work opens up the potentiality of employing complex networks for cough sound analysis, which is vital in the current scenario of COVID-19.
Ključne besede: Complex network analysis, Auscultation, Croup cough, Pertussis
Spectral analysis, Machine learning techniques
Objavljeno v RUNG: 30.06.2022; Ogledov: 2562; Prenosov: 0
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