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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: osebi
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)

Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation
SANKARARMAN S, SREEJYOTHI S, RENJINI A, RAJ VIMAL, Swapna Mohanachandran Nair Sindhu, 2020, izvirni znanstveni članek

Opis: The development of novel digital auscultation techniques has become highly significant in the context of the outburst of the pandemic COVID 19. The present work reports the spectral, nonlinear time series, fractal, and complexity analysis of vesicular (VB) and bronchial (BB) breath signals. The analysis is carried out with 37 breath sound signals. The spectral analysis brings out the signatures of VB and BB through the power spectral density plot and wavelet scalogram. The dynamics of airflow through the respiratory tract during VB and BB are investigated using the nonlinear time series and complexity analyses in terms of the phase portrait, fractal dimension, Hurst exponent, and sample entropy. The higher degree of chaoticity in BB relative to VB is unwrapped through the maximal Lyapunov exponent. The principal component analysis helps in classifying VB and BB sound signals through the feature extraction from the power spectral density data. The method proposed in the present work is simple, cost-effective, and sensitive, with a far-reaching potential of addressing and diagnosing the current issue of COVID 19 through lung auscultation.
Najdeno v: osebi
Ključne besede: Breath sound analysis, Fractal dimension, Nonlinear time series analysis, Sample entropy, Hurst exponent, Principal component analysis
Objavljeno: 28.06.2022; Ogledov: 267; Prenosov: 0
.pdf Polno besedilo (2,73 MB)

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: osebi
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)

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