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1.
Unravelling the potential of phase portrait in the auscultation of mitral valve dysfunction
SANKARARAMAN SANKARANARAYANA IYER, RAJ VIMAL, RENJINI A, SREEJYOTHI S, Swapna Mohanachandran Nair Sindhu, 2021, izvirni znanstveni članek

Opis: The manuscript elucidates the potential of phase portrait, fast Fourier transform, wavelet, and time-series analyses of the heart murmur (HM) of normal (healthy) and mitral regurgitation (MR) in the diagnosis of valve-related cardiovascular diseases. The temporal evolution study of phase portrait and the entropy analyses of HM unveil the valve dysfunctioninduced haemodynamics. A tenfold increase in sample entropy in MR from that of normal indicates the valve dysfunction. The occurrence of a large number of frequency components between lub and dub in MR, compared to the normal, is substantiated through the spectral analyses. The machine learning techniques, K-nearest neighbour, support vector machine, and principal component analyses give 100% predictive accuracy. Thus, the study suggests a surrogate method of auscultation of HM that can be employed cost-effectively in rural health centres.
Najdeno v: ključnih besedah
Ključne besede: phase portrait, auscultation, mitral valve dysfunction, heart murmur, nonlinear time series analysis
Objavljeno: 28.06.2022; Ogledov: 245; Prenosov: 0
.pdf Polno besedilo (2,02 MB)

2.
Time series and fractal analyses of wheezing
Sankaranarayana Iyer Sankararaman, S. Sreejyothi, Vimal Raj, Ammini Renjini, Mohanachandran Nair Sindhu Swapna, 2020, izvirni znanstveni članek

Opis: 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.
Najdeno v: ključnih besedah
Povzetek najdenega: ...sound opens a possibility of detection through auscultation and of employing spectral, fractal, nonlinear time...
Ključne besede: auscultation, wheeze, fractals, nonlinear time series analysis, sample entropy
Objavljeno: 30.06.2022; Ogledov: 236; Prenosov: 0
.pdf Polno besedilo (2,46 MB)

3.
Complex network-based cough signal analysis for digital auscultation: a machine learning approach
Swapna Mohanachandran Nair Sindhu, 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.
Najdeno v: ključnih besedah
Ključne besede: Complex network analysis, Auscultation, Croup cough, Pertussis Spectral analysis, Machine learning techniques
Objavljeno: 30.06.2022; Ogledov: 324; Prenosov: 0
.pdf Polno besedilo (1,71 MB)

4.
Bioacoustic signal analysis through complex network features
Sankararaman S, RAJ VIMAL, Swapna Mohanachandran Nair Sindhu, 2022, izvirni znanstveni članek

Opis: The paper proposes a graph-theoretical approach to auscultation, bringing out the potential of graph features in classifying the bioacoustics signals. The complex network analysis of the bioacoustics signals - vesicular (VE) and bronchial (BR) breath sound - of 48 healthy persons are carried out for understanding the airflow dynamics during respiration. The VE and BR are classified by the machine learning techniques extracting the graph features – the number of edges (E), graph density (D), transitivity (T), degree centrality (Dcg) and eigenvector centrality (Ecg). The higher value of E, D, and T in BR indicates the temporally correlated airflow through the wider tracheobronchial tract resulting in sustained high-intense low-frequencies. The frequency spread and highfrequencies in VE, arising due to the less correlated airflow through the narrow segmental bronchi and lobar, appears as a lower value for E, D, and T. The lower values of Dcg and Ecg justify the inferences from the spectral and other graph parameters. The study proposes a methodology in remote auscultation that can be employed in the current scenario of COVID-19.
Najdeno v: ključnih besedah
Povzetek najdenega: ...The paper proposes a graph-theoretical approach to auscultation, bringing out the potential of graph features...
Ključne besede: Bioacoustic signal, Graph theory, Complex network, Lung auscultation
Objavljeno: 30.06.2022; Ogledov: 225; Prenosov: 0
.pdf Polno besedilo (2,63 MB)

5.
Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: A machine learning approach
Swapna Mohanachandran Nair Sindhu, 2021, izvirni znanstveni članek

Opis: The paper delves into the plausibility of applying fractal, spectral, and nonlinear time series analyses for lung auscultation. The thirty-five sound signals of bronchial (BB) and pulmonary crackle (PC) analysed by fast Fourier transform and wavelet not only give the details of number, nature, and time of occurrence of the frequency components but also throw light onto the embedded air flow during breathing. Fractal dimension, phase portrait, and sample entropy help in divulging the greater randomness, antipersistent nature, and complexity of airflow dynamics in BB than PC. The potential of principal component analysis through the spectral feature extraction categorises BB, fine crackles, and coarse crackles. The phase portrait feature-based supervised classification proves to be better compared to the unsupervised machine learning technique. The present work elucidates phase portrait features as a better choice of classification, as it takes into consideration the temporal correlation between the data points of the time series signal, and thereby suggesting a novel surrogate method for the diagnosis in pulmonology. The study suggests the possible application of the techniques in the auscultation of coronavirus disease 2019 seriously affecting the respiratory system.
Najdeno v: ključnih besedah
Ključne besede: Auscultation, Biomedical signal processing, Fractals, Machine learning, Phase portrait, Pulmonary crackle.
Objavljeno: 30.06.2022; Ogledov: 255; Prenosov: 0
.pdf Polno besedilo (1,25 MB)

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