Pulmonary crackle feature extraction using tsallis entropy for automatic lung sound classification

Membrane potential changes in vocal cord tensor motoneurons during breathing, vocalization, coughing and swallowing in decerebrate cats.

The study proposes a digital stethoscope to assist physicians who are not respiratory specialists to diagnose specific diseases from lung sounds in local areas which lacks specialists and advanced medical equipment. The digital stethoscope presents a spectrogram of the auscultatory sound that illustrates changes in its feature values to visually present the area where the abnormal sound occurs. It not only assists non-specialist physicians in diagnosis but also provides easier explanations for patients. Accurate auscultation requires specialized knowledge and experience. Non-specialist physicians have difficulty distinguishing the characteristics of abnormal sounds among lung sounds which include a wide variety of sounds. Even if they find an abnormal sound suspected to be a disease, physicians will provide patients with only oral diagnosis explanations which would prevent patients from understanding their conditions in depth. The proposed method converts lung sound data collected by a digital stethoscope into a visual spectrogram showing the frequency features. The method uses the short-time Fourier transform as a method to extract the frequency features of lung sounds for each short segment in the whole time series of the lung sound. The converted spectrogram is used to detect disease-specific abnormal sounds comprehensively. The degree of abnormal sounds that appears in the inspiratory and expiratory phases varies with disease progression. The proposed method identifies abnormal sounds in the inspiratory and expiratory phases. Based on the inspiratory and expiratory phases recorded in the diagnostic by specialists, the method derives whether the abnormal sound occurs in each phase. It enables the method to detect specific respiratory diseases along with the degree of their progression. Furthermore, it also allows the method to visually present the location of detected abnormal sounds to patients. This paper uses a short-time Fourier transform as a method for extracting frequency features of lung sounds in a certain range. It also shows that feature extraction as a spectrogram that emphasizes the low frequency band, which is the human audible range, is effective in assisting the non-specialist physician in diagnosis. This paper prepares experimental data from real diagnoses so that data noise and disease features can be taken into account. The diagnostic accuracy is evaluated with a method of segmenting spectrogram images to extract frequency features at specific times. The method constructs a model for detecting fine crackles using the machine learning algorithm Decision Tree. The experimental results have shown that the accuracy of detecting fine crackles is 0.89. The high accuracy obtained from this model allows us to confirm the effectiveness of the proposed method for supporting for non-specialist physicians to distinguish abnormal sounds. The successful detection of fine crackles in the expiratory or inspiratory phase suggests that the progression degree of the disease could be estimated with the proposed digital stethoscope. The digital stethoscope will make it possible to accurately diagnose respiratory diseases along with a comprehensive explanation of diagnosis results to patients in areas with poor medical facilities. It also provides non-specialists with dependable diagnostic aid.

EPR steering is an asymmetric quantum correlation, as such, it is an important resource for quantum information protocols, like those used in quantum key distribution that are of tremendous relevance nowadays. The security of the aforementioned protocols can be shown by using entropic uncertainty measures, which are based on quantum information quantities, among which the entropies are found. In this work, we propose an entropic measure of steering, starting from the detection of steering given in a previous entropic uncertainty relation-based criterion, as well as using a generalised entropy, the Tsallis entropies. We define the entropic measure of steering in terms of an indicator, a quantity depending on the entropies of the measurements of the system, which assigns a value to the steering content of quantum states, while also comprising the asymmetry property of steering. We investigate the properties of the proposed quantity as a measure of steering and its asymmetry and show how it can be used for specific examples. For instance, a class of noisy two-qubit states that includes the Werner states is used to demonstrate the potential of such a proposal. Furthermore, a comparison is also made with the detection of steering in these states using existing steering criteria and the amount of steering a geometric measure for the Werner state gives.

This study explores the application of Tsallis entropy in evaluating uncertainty within the framework of consecutive k-out-of-n good systems, which are widely utilized in various reliability and engineering contexts. We derive new analytical expressions and meaningful bounds for the Tsallis entropy under various lifetime distributions, offering fresh insight into the structural behavior of system-level uncertainty. The approach establishes theoretical connections with classical entropy measures, such as Shannon and Rényi entropies, and provides a foundation for comparing systems under different stochastic orders. A nonparametric estimator is proposed to estimate the Tsallis entropy in this setting, and its performance is evaluated through Monte Carlo simulations. In addition, we develop a new entropy-based test for exponentiality, building on the distinctive properties of system lifetimes. So, Tsallis entropy serves as a flexible tool in both reliability characterization and statistical inference.

D1-dopamine receptor blockade slows respiratory rhythm and enhances opioid-mediated depression

The objective of the present work was to develop new computational parameters to examine the characteristics of respiratory cycle phases from the tracheal breathing sound signal during sleep. Tracheal sound data from 14 patients (10 males and 4 females) were examined. From each patient, a 10 min long section of normal and a 10 min section of flow-limited breathing during sleep were analysed. The computationally determined proportional durations of the respiratory phases were first investigated. Moreover, the phase durations and breathing sound amplitude levels were used to calculate the area under the breathing sound envelope signal during inspiration and expiration phases. An inspiratory sound index was then developed to provide the percentage of this type of area during the inspiratory phase with respect to the combined area of inspiratory and expiratory phases. The proportional duration of the inspiratory phase showed statistically significantly higher values during flow-limited breathing than during normal breathing and inspiratory pause displayed an opposite difference. The inspiratory sound index showed statistically significantly higher values during flow-limited breathing than during normal breathing. The presented novel computational parameters could contribute to the examination of sleep-disordered breathing or as a screening tool.

Indacaterol and tiotropium combination therapy in patients with chronic obstructive pulmonary disease

The role of the dorsolateral pons in the control of expiratory duration (Te) and breathing frequency is incompletely understood. A subregion of the pontine parabrachial-Kölliker-Fuse (PB-KF) complex of dogs was identified via microinjections, in which localized pharmacologically induced increases in neuronal activity produced increases in breathing rate while decreases in neuronal activity produced decreases in breathing rate. This subregion is also very sensitive to local and systemic opioids. The purpose of this study was to precisely characterize the relationship between the PB-KF subregion pattern of altered neuronal activity and the control of respiratory phase timing as well as the time course of the phrenic nerve activity/neurogram (PNG). Pulse train electrical stimulation patterns synchronized with the onset of the expiratory (E) and/or phrenic inspiratory (I) phase were delivered via a small concentric bipolar electrode while the PNG was recorded in decerebrate, vagotomized dogs. Step frequency patterns during the E phase produced a marked frequency-dependent decrease in Te, while similar step inputs during the I phase increased inspiratory duration (Ti) by 14 ± 3%. Delayed pulse trains were capable of pacing the breathing rate by terminating the E phase and also of triggering a consistent stereotypical inspiratory PNG pattern, even when evoked during apnea. This property suggests that the I-phase pattern generator functions in a monostable circuit mode with a stable E phase and a transient I phase. Thus the I-pattern generator must contain neurons with nonlinear pacemaker-like properties, which allow the network to rapidly obtain a full on-state followed by relatively slow inactivation. The activated network can be further modulated and supplies excitatory drive to the neurons involved with pattern generation.NEW & NOTEWORTHY A circumscribed subregion of the pontine medial parabrachial nucleus plays a key role in the control of breathing frequency primarily via changes in expiratory duration. Excitation of this subregion triggers the onset of the inspiratory phase, resulting in a stereotypical ramplike phrenic activity pattern independent of time within the expiratory phase. The ability to pace the I-burst rate suggests that the in vivo I-pattern generating network must contain functioning pacemaker neurons.

Aerodynamics of Sneezing

Investigation of the symbolic DNA sequence in terms of its structure and organization is a challenging problem. Inherent uncertainty and sequence variability makes information theoretic framework eminently suitable for applying to variety of computational biology problems. This paper highlights the properties of parametric and non-parametric entropy measures and focusses on few applications where entropic measures have been used. The link between Tsallis entropy and power-law is drawn using maximum entropy principle in capturing the well-known long-range dependence in DNA sequences.KeywordsFractional Brownian MotionAverage Mutual InformationEntropy MeasureSequence VariabilityMaximum Entropy PrincipleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

In this article, we study the helium atom confined in a spherical impenetrable cavity by using informational measures. We use the Ritz variational method to obtain the energies and wave functions of the confined helium atom as a function of the cavity radius . As trial wave functions we use one uncorrelated function and five explicitly correlated basis sets in Hylleraas coordinates with different degrees of electronic correlation. We computed the Shannon entropy, Fisher information, Kullback–Leibler entropy, Tsallis entropy, disequilibrium and Fisher–Shannon complexity, as a function of . We found that these entropic measures are sensitive to electronic correlation and can be used to measure it. As expected these entropic measures are less sensitive to electron correlation in the strong confinement regime ( a.u.).

A supersaturated design is a factorial design in which the number of effects to be estimated is greater than the number of runs. It is used in many experiments, for screening purpose, i.e., for studying a large number of factors and identifying the active ones. In this paper, we propose a method for screening out the important factors from a large set of potentially active variables through the symmetrical uncertainty measure combined with the information gain measure. We develop an information theoretical analysis method by using Shannon and some other entropy measures such as Renyi entropy, Havrda–Charvat entropy, and Tsallis entropy, on data and assuming generalized linear models for a Bernoulli response. This method is quite advantageous as it enables us to use supersaturated designs for analyzing data on generalized linear models. Empirical study demonstrates that this method performs well giving low Type I and Type II error rates for any entropy measure we use. Moreover, the proposed method is more efficient when compared to the existing ROC methodology of identifying the significant factors for a dichotomous response in terms of error rates.

The compounded Bell generalized class of distributions is proposed in this article as an alternative to the compounded Poisson generalized family of distributions. Some properties and actuarial measures are presented. The properties of a special model named Bell Weibull (BellW) are obtained such as the linear representation of density, rth moment, incomplete moment, moment generating function using Wright generalized hypergeometric function and Meijer’s G function, the pth moment of order statistics, reliability, stochastic ordering, and residual and reversed residual life. Moreover, some commonly used entropy measures, namely, Rényi, Havrda and Charvat, and Arimoto and Tsallis entropy are obtained for the special model. From the inferential side, parameters are estimated using maximum likelihood estimation. The simulation study is performed to highlight the behavior of estimates. Some actuarial measures including expected shortfall, value at risk, tail value at risk, tail variance, and tail variance premium for the BellW model are presented with the numerical illustration. The usefulness of the proposed family is evaluated using insurance claims and COVID-19 datasets. Convincing results are obtained.

This study investigates systemic risk, return patterns, and diversification within the Johannesburg Stock Exchange (JSE) during the COVID-19 pandemic, utilizing data-centric approaches and the ARMA-GARCH vine copula-based conditional value-at-risk (CoVaR) model. By comparing three investment strategies—industry sector-based, asset risk–return plot-based, and clustering-based—this research reveals that the industrial and technology sectors show no ARCH effects and remain isolated from other sectors, indicating potential diversification opportunities. Furthermore, the analysis employs C-vine and R-vine copulas, which uncover weak tail dependence among JSE sectors. This finding suggests that significant fluctuations in one sector minimally impact others, thereby highlighting the resilience of the South African economy. Additionally, entropy measures, including Shannon and Tsallis entropy, provide insights into the dynamics and predictability of various portfolios, with results indicating higher volatility in the energy sector and certain clusters. These findings offer valuable guidance for investors and policymakers, emphasizing the need for adaptable risk management strategies, particularly during turbulent periods. Notably, the industrial sector’s low CoVaR values signal stability, encouraging risk-tolerant investors to consider increasing their exposure. In contrast, others may explore diversification and hedging strategies to mitigate risk. Interestingly, the industry sector-based portfolio demonstrates better diversification during the COVID-19 crisis than the other two data-centric portfolios. This portfolio exhibits the highest Tsallis entropy, suggesting it offers the best diversity among the types analyzed, albeit said diversity is still relatively low overall. However, the portfolios based on groups and clusters of sectors show similar levels of diversity and concentration, as indicated by their identical entropy values.

In order to investigate the influence of respiratory frequency in the measurement of specific airway conductance (sGaw) during non-panting breathing, we examined specific airway conductance using a body plethysmograph (BX-82, Minato) in 20 stable pulmonary emphysema patients and 19 normal control subjects. Our body plethysmograph device can measure sGaw automatically without panting by making various corrections using a 16-bit microcomputer. We measured sGaw first at a flow of 0.5 L/sec during both inspiratory and expiratory ventilatory phases, then the respiratory frequency was changed from 0.5 to 1.0, 1.5 and 2.0 Hz. In normal control subjects sGaw, tidal volume and mouth flow significantly changed by increasing respiratory frequency, and there was a significant correlation between sGaw and mouth flow. In pulmonary emphysema patients, tidal volume decreased significantly by increasing respiratory frequency, and there was a significant correlation between sGaw and tidal volume, but sGaw and mouth flow did not change significantly by increasing respiratory frequency. These results suggest that specific airway conductance is influenced by respiratory frequency, possibly due to the change in tidal volume and mouth flow. It is necessary to standardize respiratory frequencies and mouth flows in the measurement of specific airway conductance during non-panting breathing.