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4. 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: 2098; Prenosov: 7
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6. Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: A machine learning approachMohanachandran Nair Sindhu Swapna, 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.
Ključne besede: Auscultation, Biomedical signal processing, Fractals, Machine learning, Phase portrait, Pulmonary crackle.
Objavljeno v RUNG: 30.06.2022; Ogledov: 2079; Prenosov: 0
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7. Fractal and time-series analyses based rhonchi and bronchial auscultation: A machine learning approachMohanachandran Nair Sindhu Swapna, 2022, izvirni znanstveni članek Opis: Objectives: The present work reports the study of 34 rhonchi (RB) and
Bronchial Breath (BB) signals employing machine learning techniques, timefrequency, fractal, and non-linear time-series analyses. Methods: The timefrequency analyses and the complexity in the dynamics of airflow in BB
and RB are studied using both Power Spectral Density (PSD) features and
non-linear measures. For accurate prediction of these signals, PSD and nonlinear measures are fed as input attributes to various machine learning
models. Findings: The spectral analyses reveal fewer, low-intensity frequency
components along with its overtones in the intermittent and rapidly damping
RB signal. The complexity in the dynamics of airflow in BB and RB is investigated
through the fractal dimension, Hurst exponent, phase portrait, maximal
Lyapunov exponent, and sample entropy values. The greater value of entropy
for the RB signal provides an insight into the internal morphology of the airways
containing mucous and other obstructions. The Principal Component Analysis
(PCA) employs PSD features, and Linear Discriminant Analysis (LDA) along
with Pattern Recognition Neural Network (PRNN) uses non-linear measures
for predicting BB and RB. Signal classification based on phase portrait
features evaluates the multidimensional aspects of signal intensities, whereas
that based on PSD features considers mere signal intensities. The principal
components in PCA cover about 86.5% of the overall variance of the data class,
successfully distinguishing BB and RB signals. LDA and PRNN that use nonlinear time-series parameters identify and predict RB and BB signals with 100%
accuracy, sensitivity, specificity, and precision. Novelty: The study divulges the
potential of non-linear measures and PSD features in classifying these signals
enabling its application to be extended for low-cost, non-invasive COVID-19
detection and real-time health monitoring.
Ključne besede: lung signal, fractal analysis, sample entropy, nonlinear timeseries, machine learning techniques
Objavljeno v RUNG: 30.06.2022; Ogledov: 2549; Prenosov: 0
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8. Bioacoustic signal analysis through complex network featuresMohanachandran Nair Sindhu Swapna, RAJ VIMAL, Sankararaman S, 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 high-frequencies 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.
Ključne besede: Bioacoustic signal, Graph theory, Complex network, Lung auscultation
Objavljeno v RUNG: 30.06.2022; Ogledov: 2284; Prenosov: 0
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9. Downscaling of sample entropy of nanofluids by carbon allotropes : a thermal lens studyMohanachandran Nair Sindhu Swapna, Vimal Raj, S. Sreejyothi, K. Satheesh Kumar, Sankaranarayana Iyer Sankararaman, 2020, izvirni znanstveni članek Opis: The work reported in this paper is the first attempt to delineate the molecular or particle dynamics from the thermal lens signal of carbon allotropic nanofluids (CANs), employing time series and fractal analyses. The nanofluids of multi-walled carbon nanotubes and graphene are prepared in base fluid, coconut oil, at low volume fraction and are subjected to thermal lens study. We have studied the thermal diffusivity and refractive index variations of the medium by analyzing the thermal lens (TL) signal. By segmenting the TL signal, the complex dynamics
involved during its evolution is investigated through the phase portrait, fractal dimension, Hurst exponent, and sample entropy using time series and fractal analyses. The study also explains how the increase of the photothermal energy turns a system into stochastic and anti-persistent. The sample entropy (S) and refractive index analyses of the TL signal by segmenting into five regions reveal the evolution of S with the increase of enthalpy. The lowering of S in CAN along with its thermal diffusivity (50%–57% below) as a result of heat-trapping suggests
the technique of downscaling sample entropy of the base fluid using carbon allotropes and thereby opening a novel method of improving the efficiency of thermal systems.
Ključne besede: carbon allotropic nanofluids, time series, entropy, MWCNT, thermal lens signal
Objavljeno v RUNG: 30.06.2022; Ogledov: 2475; Prenosov: 0
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10. First upper limits on the radar cross section of cosmic-ray induced extensive air showersR.U. Abbasi, Jon Paul Lundquist, 2017, izvirni znanstveni članek Opis: TARA (Telescope Array Radar) is a cosmic ray radar detection experiment colocated with Telescope Array, the conventional surface scintillation detector (SD) and fluorescence telescope detector (FD) near Delta, Utah, U.S.A. The TARA detector combines a 40 kW, 54.1 MHz VHF transmitter and high-gain transmitting antenna which broadcasts the radar carrier over the SD array and within the FD field of view, towards a 250 MS/s DAQ receiver. TARA has been collecting data since 2013 with the primary goal of observing the radar signatures of extensive air showers (EAS). Simulations indicate that echoes are expected to be short in duration (∼ 10 µs) and exhibit rapidly changing frequency, with rates on the order 1 MHz/µs. The EAS radar cross-section (RCS) is currently unknown although it is the subject of over 70 years of speculation. A novel signal search technique is described in which the expected radar echo of a particular air shower is used as a matched filter template and compared to waveforms obtained by triggering the radar DAQ using the Telescope Array fluorescence detector. No evidence for the scattering of radio frequency radiation by EAS is obtained to date. We report the first quantitative RCS upper limits using EAS that triggered the Telescope Array Fluorescence Detector.
Ključne besede: Cosmic ray, Radar, Digital signal processing, Radar cross-section
Objavljeno v RUNG: 27.04.2020; Ogledov: 3912; Prenosov: 0
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