Temporal evolution of sample entropy in thermal lens system

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.

Complex network analysis of the thermal lens signal: a Markov model approach

The paper reports a novel, to the best of my knowledge, complex network-based Markov model approach to analyze the thermal lens (TL) signal. The complex network is constructed by segmenting the experimental and fitted TL signals into three regions. Here, a new parameter, degree of fluctuation, is introduced to analyze the Markov transition probability matrix (M) and the increase of system enthalpy leading to increased Brownian motion. The spread of data about the diagonal elements of M distinguishes the experimental and fitted data and appears as an increased number of edges in the complex network. Thus, the system's network displays the transient heat flow giving information about the Brownian motion in the medium.

Unwrapping the phase portrait features of adventitious crackle for auscultation and classification: a machine learning approach

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.

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.

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.

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.