Bioacoustic signal analysis through complex network features.

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.

Acclimatization through thermal diffusivity tuning of coconut oil - A mode mismatched dual-beam thermal lens study.

BACKGROUND: Ayurvedic medicinal oils traditionally prepared by blending herbal extracts in different compositions are commonly used for treatment and improving health. The estimation of the thermal properties of medicinal oils is essential for practical applications. OBJECTIVE: The present work aims to expound the ability of medicinal oils for the acclimatization of body temperature by determining its thermal diffusivity and thereby providing a validation to the traditional knowledge. MATERIALS AND METHODS: The medicinal oils are prepared by incorporating black pepper (Piper nigrum), aloe vera (Aloe barbadensis), hibiscus bud (Hibiscus rosa-sinensis) and Ocimum sanctum in coconut oil base. The samples are subjected to thermal diffusivity study using the mode-mismatched dual-beam thermal lens technique. RESULTS: The study reveals that the incorporation of black pepper (Piper nigrum), having hot potency (Ushna veerya), to the base fluid lowers the thermal diffusivity value, suggesting its potential in heat-trapping. The addition of aloe vera (Aloe barbadensis), hibiscus bud (Hibiscus rosa-sinensis), and O. sanctum dissipates heat energy quickly, thus increases the thermal diffusivity of coconut oil revealing a cold potency (Sheeta veerya). The study provides a validation for traditional knowledge and delineates the possiblity of thermal diffusivity tuning of the base fluids. CONCLUSION: The thermal diffusivity tuning through incorporation of herbal extracts can effectively be used to acclimatize the human body temperature with the surroundings. A higher thermal diffusivity value induces a cooling effect and the lower value causes heating effect. This, opens up the possibility of using thermally tuned oils depending on climate and geographical location.

Graph-based feature extraction and classification of wet and dry cough signals: a machine learning approach.

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.

Phase portrait for high fidelity feature extraction and classification: A surrogate approach.

This paper proposes a novel surrogate method of classification of breath sound signals for auscultation through the principal component analysis (PCA), extracting the features of a phase portrait. The nonlinear parameters of the phase portrait like the Lyapunov exponent, the sample entropy, the fractal dimension, and the Hurst exponent help in understanding the degree of complexity arising due to the turbulence of air molecules in the airways of the lungs. Thirty-nine breath sound signals of bronchial breath (BB) and pleural rub (PR) are studied through spectral, fractal, and phase portrait analyses. The fast Fourier transform and wavelet analyses show a lesser number of high-intense, low-frequency components in PR, unlike BB. The fractal dimension and sample entropy values for PR are, respectively, 1.772 and 1.041, while those for BB are 1.801 and 1.331, respectively. This study reveals that the BB signal is more complex and random, as evidenced by the fractal dimension and sample entropy values. The signals are classified by PCA based on the features extracted from the power spectral density (PSD) data and the features of the phase portrait. The PCA based on the features of the phase portrait considers the temporal correlation of the signal amplitudes and that based on the PSD data considers only the signal amplitudes, suggesting that the former method is better than the latter as it reflects the multidimensional aspects of the signal. This appears in the PCA-based classification as 89.6% for BB, a higher variance than the 80.5% for the PR signal, suggesting the higher fidelity of the phase portrait-based classification.

Time series and fractal analyses of wheezing: a novel approach.

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 reflected through the lung sound opens a possibility of detection through auscultation and of employing spectral, fractal, nonlinear time series and principal component analyses. Thirty-five 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.

Nonlinear time series and principal component analyses: Potential diagnostic tools for COVID-19 auscultation.

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.

Downscaling of sample entropy of nanofluids by carbon allotropes: A thermal lens study.

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.

Temporal evolution of sample entropy in thermal lens system.

The photothermal phenomenon resulting in thermal lens formation in liquid media involves complex molecular dynamics responsible for temperature and refractive index variation. As a thermodynamic system, the entropy of the medium also changes. In this paper, the time series and phase portrait analysis of the thermal lens signal is carried out to understand the molecular dynamics. The study reveals the increase in complexity, disorder, and antipersistance nature through fractal dimension, sample entropy, and Hurst exponent, respectively. The analysis of the signal on segmentation reveals the evolution of sample entropy and the stochastic nature of the system with time. The phase portrait analysis also is in support of these observations. Thus, the study suggests that the temporal evolution of sample entropy is similar to the temperature-dependent refractive index.

Optical emission diagnosis of carbon nanoparticle-incorporated chlorophyll for sensing applications.

Chlorophyll fluorescence (Chl F) is widely used in sensing applications to understand terrestrial vegetation and environmental and climatic variations. The increasing rates of industrialization and carbon emission from internal combustion engines (ICEs) pose a threat to sustainable development. This study analyses the impact of carbon nanoparticles (CNPs) from ICEs on the optical absorption and fluorescence emission of leaf pigments. Leaf pigments without and with CNPs were subjected to UV-visible and photoluminescence (PL) spectroscopy analyses. The field emission scanning electron microscopy and high-resolution transmission electron microscopy images of CNPs showed their morphology. The Jablonski diagram of the CNP-incorporated chlorophyll system helped in understanding the fluorescence emission, internal conversion, and the exchange of energy between them. The variations in (i) total chlorophyll, (ii) optical absorbance by total chlorophyll, (iii) PL emission peak (at 675 nm and 718 nm) intensities for different excitation wavelengths, and (iv) normalized absorbance at the PL emission peaks with different CNP concentrations were analysed by dividing into three regions. In Region I (0-0.625 mg ml-1), the radiative component dominated the nonradiative component as a result of energy transfer from CNPs to chlorophyll. In Region II (0.625-1.2 mg ml-1), the increase in CNP concentration initiated diffusion into chloroplasts, resulting in the increase in the nonradiative part of total energy and decrease in PL peak intensity. In Region III (1.2-2.5 mg ml-1), the energy absorbed by the CNPs dissipated more nonradiatively, leading to a slow rate of increase in the radiative part. The visual response of PL emission, color purity, and the distribution of the emitted energy over the spectrum studied with the help of CIE plots, power spectrum, and confocal fluorescence microscopy revealed the fluorescence emission in the red region. This study suggests the possibility of employing Chl F in agricultural, environmental, and biological fields for sensing applications.

Raman spectroscopic and fractal analysis of blood samples of dengue fever patients.

BACKGROUND: The limitations of the existing techniques for the early detection of dengue fever necessitate the development of a powerful optical technique. OBJECTIVE: The present work is a study of Raman spectral modifications of blood on dengue infection and thereby to develop a spectroscopic method for its early detection. The images of the samples are subjected to fractal analysis to find the variation of fractal dimensions on dengue infection. METHODS: Correlation of platelet counts of dengue infected blood with Raman spectrum modification and fractal dimension. The effect of lowering of blood platelet count due to dengue infection is found to show some interesting changes in the spectrum. RESULTS: The full width at half maximum (FWHM) of the two bands in the region 950-1200 cm-1 increase with the decrease of blood platelet count. The increase in fractal dimension gives an indication of the decrease of platelet count and hence the dengue infection. CONCLUSIONS: Raman spectrum and fractal analysis can effectively be used as potential techniques for the early detection of dengue infection.

From Futile to Fruitful: Diesel Soot as White Light Emitter.

The present work describes a solution for the effective use of the hazardous particulate matter (diesel soot) from the internal combustion engines (ICEs) as a potential material emitting white light for white light emitting diodes (WLEDs). The washed soot samples are subjected to Field Emission Scanning Electron Microscopy (FESEM), High- Resolution Transmission Electron Microscopy (HR-TEM), Energy Dispersive Spectroscopy (EDS), UV-Visible, Photoluminescent (PL) Spectroscopy and quantum yield measurements. The CIE plot and Correlated Color Temperature (CCT) reveals the white fluorescence on photoexcitation. The sample on ultraviolet (UV) laser excitation, provides a visual confirmation of white light emission from the sample. The diesel soot collected from public transport buses of different years of manufacture invariably exhibit white fluorescence at an excitation of 350 nm. The sample show a quantum yield of 47.09%. The study is significant in the context of pollution and search for low-cost, rare-earth phosphor free material for white light emission and thereby turning the hazardous, futile material into a fruitful material that can be used for potential applications in photonics and electronics.