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3. Time series and fractal analyses of wheezing : a novel approachMohanachandran Nair Sindhu Swapna, Ammini Renjini, Vimal Raj, S. Sreejyothi, Sankaranarayana Iyer Sankararaman, 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.
Ključne besede: auscultation, wheeze, fractals, nonlinear time series analysis, sample entropy
Objavljeno v RUNG: 30.06.2022; Ogledov: 1237; Prenosov: 0
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4. Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultationMohanachandran Nair Sindhu Swapna, RAJ VIMAL, RENJINI A, SREEJYOTHI S, SANKARARMAN S, 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.
Ključne besede: Breath sound analysis, Fractal dimension, Nonlinear time series analysis, Sample entropy, Hurst exponent, Principal component analysis
Objavljeno v RUNG: 28.06.2022; Ogledov: 1477; Prenosov: 0
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5. Unravelling the potential of phase portrait in the auscultation of mitral valve dysfunctionMohanachandran Nair Sindhu Swapna, SREEJYOTHI S, RENJINI A, RAJ VIMAL, SANKARARAMAN SANKARANARAYANA IYER, 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.
Ključne besede: phase portrait, auscultation, mitral valve dysfunction, heart murmur, nonlinear time series analysis
Objavljeno v RUNG: 28.06.2022; Ogledov: 1155; Prenosov: 0
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6. Thermal Lensing of Multi-Walled Carbon Nanotube Solutions as Heat-Transfer NanofluidsMohanachandran Nair Sindhu Swapna, RAJ VIMAL, CABRERA HUMBERTO, SANKARARAMAN SANKARANARAYANA IYER, 2021, izvirni znanstveni članek Opis: This paper unwraps nanofluids’ particle dynamics with
multi-walled carbon nanotubes (MWCNTs) in base fluids such as
acetone, water, and ethylene glycol. Having confirmed the
morphology and structure of the MWCNTs by field emission
scanning electron microscopy, X-ray diffraction, and Raman
spectroscopic analyses, the nanofluids are prepared in three different concentrations. The nonzero absorbance at the laser wavelength, revealed through the UV−visible spectrum, makes the thermal diffusivity study of the sample by the sensitive nondestructive single beam thermal lens (TL) technique possible. The TL signal analysis by time series and fractal techniques divulges the complex particle dynamics, through phase portrait, sample entropy, fractal dimension, and Hurst exponent. The study unveils the effect of the amount of nanoparticles and the viscosity of the medium on thermal diffusivity and particle dynamics. The observed inverse relation between thermal diffusivity and viscosity is in good agreement with the
Sankar−Swapna model. The complexity of particle dynamics in MWCNT nanofluids reflected through sample entropy, and fractal
dimension shows an inverse relation to the base fluid’s viscosity. This paper investigates the role of viscosity of the base fluid on particle dynamics and thermal diffusivity of the nanofluid to explore its applicability in various thermal systems, thereby suggesting a method to tune the sample entropy through proper selection of base fluid.
Ključne besede: MWCNT, thermal lens, fractals, nonlinear time series, phase portrait, sample entropy
Objavljeno v RUNG: 28.06.2022; Ogledov: 1342; Prenosov: 0
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