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The present study investigates the instantaneous coupling among the cardiac, vascular, and respiratory systems, using the heart rate, respiration, and systolic and diastolic blood pressure variability in 12 healthy and 16 vasovagal syncope female subjects during a head-up tilt (HUT) testing protocol at 70° This study contributes to the coupling analysis by using a nonlinear joint symbolic dynamics (JSD) in a high-temporal resolution scheme, based on 5 min segments of the time series that are shifted every minute. For each segment, a bivariate JSD matrix was constructed to obtain global and local coupling indices in accordance to Shannon's entropy and the probability of occurrence of various bivariate words, respectively. The novel approach revealed important findings in the coupling dynamics of the systems, thus allowing the detection of group differences during the early orthostatic phase, and during the HUT test, before the occurrence of any pre-syncopal symptoms. In patients, the global indices indicated a significant decrease of cardiovascular coupling, starting at 10 min after the tilt-up, manifested by reduced baroreflex sensitivity and cardiorespiratory coupling that was initiated 8 min after the onset of the orthostatic phase (OP). A decreased autonomic control on cardiovascular-respiratory couplings was further evidenced by increased alterations of the JSD indices during the OP compared to the supine position in patients compared to controls. Furthermore, findings based on local indices demonstrated that female patients showed reductions and disengagements in cardiovascular (p < 0.001) and cardiorespiratory (p < 0.01) couplings, as early as the first 5 min and during the complete OP.

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Resumen: En este trabajo se presenta un método para calcular los niveles de fibrosis pulmonar en imágenes de tomografía axial computarizada. Se utilizó un algoritmo de segmentación semiautomática basado en el método de Chan-Vese. El método mostró similitudes de forma cualitativa en la región de la fibrosis con respecto al experto clínico. Sin embargo es necesario validar los resultados con una base de datos mayor. El método propuesto aproxima un porcentaje de fibrosis de forma fácil para apoyar su implementación en la práctica clínica minimizando la subjetividad del experto médico y generando una estimación cuantitativa de la región de fibrosis.

Abstract: A method to estimate the pulmonary fibrosis in computed tomography (CT) imaging is presented. A semi-automatic segmentation algorithm based on the Chan-Vese method was used. The proposed method shows a similar fibrosis región with respect to clinical expert. However, the results need to be validated in a bigger data base. The proposed method approximates a fibrosis percentage that allows to achieve this procedure easily in order to support its implementation in the clinical practice minimizing the clinical expert subjectivity and generating a quantitative estimation of fibrosis region.

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In studies of autonomic regulation during orthostatic challenges only a few nonlinear methods have been considered without investigating the effect of gender in young controls. Especially, the temporal development of the autonomic regulation has not yet been explicitly analyzed using short-term segments in supine position, transition and orthostatic phase (OP). In this study, nonlinear analysis of cardiovascular and respiratory time series was performed to investigate how nonlinear indices are dynamically changing with respect to gender during orthostatic challenges. The analysis was carried out using shifted short-term segments throughout a head-up tilt test in 24 healthy subjects, 12 men (26 ± 4 years) and 12 age-matched women (26 ± 5 years), at supine position and during OP at 70°. The nonlinear methods demonstrated statistical differences in the autonomic regulation between males and females. Orthostatic stress caused significantly decreased heart rate variability due to increased sympathetic activity mainly in men, already at the beginning and during the complete OP, revealed by (a) increased occurrence of specific word types with constant fluctuations as pW111 from symbolic dynamics, (b) augmented fractal correlation properties by the short-term index alpha1 from detrended fluctuation analysis, (c) increased slope indices (21ati and 31ati) from auto-transinformation and (d) augmented time irreversibility indices demonstrating more temporal asymmetries and nonlinear dynamics in men than in women. After tilt-up, both men and women increased their sympathetic activity but in a different way. Time-dependent gender differences during orthostatic challenge were shown directly between men and women or indirectly comparing baseline and different temporal stages of OP. The proposed dynamical study of autonomic regulation has the advantage of screening the fluctuations of the sympathetic and vagal activities that can be quantified by the temporal behavior of nonlinear indices. The findings in this paper strongly suggest the need for gender separation in studies of the dynamics of autonomic regulation during orthostatic challenge.

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The cardiovascular and respiratory autonomic nervous regulation has been studied mainly by hemodynamic responses during different physical stressors. In this study, dynamics of autonomic response to an orthostatic challenge was investigated by hemodynamic variables and by diverse linear and nonlinear indices calculated from time series of beat-to-beat intervals (BBI), respiratory cycle duration (RESP), systolic (SYS) and diastolic (DIA) blood pressure. This study included 16 young female patients (SYN) with vasovagal syncope and 12 age-matched female controls (CON). The subjects were enrolled in a head-up tilt (HUT) test, breathing normally, including 5min of baseline (BL, supine position) and 18min of 70° orthostatic phase (OP). To increase the time resolution of the analysis the time series were segmented in five-minute overlapping windows with a shift of 1min. Hemodynamic parameters did not show any statistical differences between SYN and CON. Time domain linear analysis revealed increased respiratory frequency and increased blood pressure variability (BPV) in patients during OP meaning increased sympathetic activity and vagal withdrawal. Frequency domain analysis confirmed a predominance of sympathetic tone by steadily increased values of low over high frequency power in BBI and of low frequency power in SYS and DIA in patients during OP. The nonlinear analysis by symbolic dynamics seemed to be highly suitable for differentiation of SYN and CON in the early beginning of OP, i.e., 5min after tilt-up. In particular the index SYS_plvar3 showed less patterns of low variability in patients reflecting a steadily increase in both BPV and sympathetic activity. The proposed dynamical analysis could lead to a better understanding of the temporal underlying mechanisms in healthy subjects and patients under orthostatic stress.

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A step forward in the knowledge about the underlying physiological phenomena of thoracic sounds requires a reliable estimate of their time-frequency behavior that overcomes the disadvantages of the conventional spectrogram. A more detailed time-frequency representation could lead to a better feature extraction for diseases classification and stratification purposes, among others. In this respect, the aim of this study was to look for an omnibus technique to obtain the time-frequency representation (TFR) of thoracic sounds by comparing generic goodness-of-fit criteria in different simulated thoracic sounds scenarios. The performance of ten TFRs for heart, normal tracheal and adventitious lung sounds was assessed using time-frequency patterns obtained by mathematical functions of the thoracic sounds. To find the best TFR performance measures, such as the 2D local (ρ(mean)) and global (ρ) central correlation, the normalized root-mean-square error (NRMSE), the cross-correlation coefficient (ρ(IF)) and the time-frequency resolution (res(TF)) were used. Simulation results pointed out that the Hilbert-Huang spectrum (HHS) had a superior performance as compared with other techniques and then, it can be considered as a reliable TFR for thoracic sounds. Furthermore, the goodness of HHS was assessed using noisy simulated signals. Additionally, HHS was applied to first and second heart sounds taken from a young healthy male subject, to tracheal sound from a middle-age healthy male subject, and to abnormal lung sounds acquired from a male patient with diffuse interstitial pneumonia. It is expected that the results of this research could be used to obtain a better signature of thoracic sounds for pattern recognition purpose, among other tasks.

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OBJECTIVE: Pulmonary disorders are frequently characterized by the presence of adventitious sounds added to the breathing or base lung sound (BLS). The aim of this work was to assess the features of BLS in extrinsic allergic alveolitis (EAA) patients in comparison to healthy subjects, applying linear and nonlinear analysis techniques. METHODS: We investigated the multichannel lung sounds on the posterior chest of 16 females, 8 healthy and 8 EAA patients, when breathing at 1.5 L/s. BLS linear features were obtained from the power spectral density (PSD) while nonlinear features were extracted by the concepts of irregularity and complexity, i.e., spectral, sample and multiscale entropy. RESULTS: The results demonstrated that spectral percentiles of BLS were lower in EAA patients than in healthy subjects but statistical significance (p<0.05) was obtained only for expiration at the left apical and both basal regions. Also, the maximum amplitude of the PSD in patients reached statistical significance ( p < 0.05) for the expiratory phase at basal regions. In the case of nonlinear techniques, significant lower values ( p < 0.05) were obtained for EAA patients during both respiratory phases at left apical and both basal regions. CONCLUSION: In conclusion, we found that BLS in chronic EAA patients is characterized by lower spectral percentiles, lower irregularity and lower complexity than in healthy subjects suggesting the feasibility of its clinical usefulness by screening its temporal alteration.

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Blind source separation by independent component analysis has been applied extensively in the biomedical field for extracting different contributing sources in a signal. Regarding lung sounds analysis to isolate the adventitious sounds from normal breathing sound is relevant. In this work the performance of FastICA, Infomax, JADE and TDSEP algorithms was assessed using different scenarios including simulated fine and coarse crackles embedded in recorded normal breathing sounds. Our results pointed out that Infomax obtained the minimum Amari index (0.10037) and the maximum signal to interference ratio (1.4578e+009). Afterwards, Infomax was applied to 25 channels of recorded normal breathing sound where simulated fine and coarse crackles were added including acoustic propagation effects. A robust blind crackle separation could improve previous results in generating an adventitious acoustic thoracic imaging.

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This work deals with the assessment of different parameterization techniques for lung sounds (LS) acquired on the whole posterior thoracic surface for normal versus abnormal LS classification. Besides the conventional technique of power spectral density (PSD), the eigenvalues of the covariance matrix and both the univariate autoregressive (UAR) and the multivariate autoregressive models (MAR) were applied for constructing feature vectors as input to a supervised neural network (SNN). The results showed the effectiveness of the UAR modeling for multichannel LS parameterization, using new data, with classification accuracy of 75% and 93% for healthy subjects and patients, respectively.

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OBJECTIVE: To assess the feasibility to generate a confident image of normal breath sounds (BS) based on the quantitative analysis of multichannel sensors and imaging them in three known clinical classes, i.e., tracheal, bronchial and vesicular, identifying their spatial distribution with high resolution on the posterior thoracic surface. METHODS: Three parametrization techniques, the percentile frequencies, the univariate AR modeling, and the eigenvalues of the covariance matrix were evaluated when applied to BS. These sounds were acquired in twelve healthy subjects by a 5x5 sensor array on the posterior thoracic surface plus the sound at the tracheal position, to obtain feature vectors that fed a supervised multilayer neural network. Based on BS classification rate, the spatial distribution of each BS class was obtained by constructing an image using deterministic interpolation. RESULTS: The univariate AR modeling was the best parametrization technique producing a classification performance of 96% during the validation phase and just 4% of not classified feature vectors. Corresponding values for the percentile frequencies were 92% and 7.7%, whereas for the eigenvalues were 91% and 9.0%. CONCLUSION: This work shows that it is possible to generate confident images associated with the distribution of normal BS classes. Therefore, a detailed image about the spatial distribution of BS in humans might be helpful for detecting lung diseases.

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Abnormal lung sounds in diffuse interstitial pneumonia have been characterized by the presence of crackles. However, few attempts have tried to analyze the sound where crackles are immersed. In this work base lung sounds (BLS) were analyzed by linear and nonlinear techniques to find possible differences between normal subjects and patients with diffuse interstitial pneumonia. In both groups, segments of lung sounds (crackles absent) and segments of BLS (lung sound in between crackles) were analyzed from acquired lung sounds from four points at the posterior chest, two apical and two basal. In this study, 8 healthy subjects and 8 patients participated and BLS were analyzed by spectral percentiles and sample entropy. Although spectral percentiles and sample entropy (SampEn) tended to be lower in the group of patients, statistical differences (p0.05) between normal subjects and patients were found in BLS at the left hemithorax at basal and apical regions, while at the right hemithorax significant differences were found only at the basal region using the nonlinear technique. We conclude that in respect to normal subjects, BLS of patients with diffuse interstitial pneumonia present differences as assessed by SampEn, so that BLS by itself provides useful information. Moreover, it seems that nonlinear technique did a better discrimination of abnormal BLS than spectral percentile parameters.

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Several researches have tried to provide a means to analyze the second heart sound (S2) in an attempt to understand the functional mechanisms in its genesis and for diagnosis purposes. In this work we tested Time-Frequency Representation (TFR) for simulated S2 selecting and applying classical and modern TFRs such as the Spectrogram, the Wigner-Ville Distribution, the Time Varying Autoregressive (TVAR) model, the Scalogram, and the Hilbert-Huang Spectrum (HHS) by Empirical Mode Decomposition. Two performance measures are proposed, the first one based on local 2D correlations (rho) between the ideal and the estimated TFRs images, while the second one based on time moments of the TFR images to provide the normalized root-mean-square error (NRMSE). Under no noise conditions, the TFRs by HHS and the TVAR modeling, by the Burg algorithm, resulted in a rho(average) of 0.788 and 0.812, and NRMSE of 0.172 and 0.195, respectively. Therefore, based on the lowest NRMSE, HHS was considered the TFR with the best performance. Afterward, HHS was applied to real S2 acquired at the aortic and pulmonary focal points.

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It is now widely accepted that crackles are associated with different pulmonary pathologies and different efforts have been done to detect and to extract them. Consequently, due to the difficulty for their characterization, the selection of an adequate time-frequency representation (TFR) for the analysis of their time-frequency dynamics is important. Traditionally, normal and abnormal lung sounds have been analyzed by the Spectrogram (SP). However, this analysis tool has certain disadvantages when one deals with nonstationary signals. As an effort to point out the appropriate analysis tool for crackles, this paper shows the performance of the Hilbert-Huang spectrum (HHS) for the analysis of fine and coarse crackles, simulated and real ones. The HHS allowed to analyze the evolving time-frequency of crackle sounds straightforward with good resolution compared with SP. Beside this enhanced time-frequency course, HHS could be useful to establish a signature to detect and separate fine from coarse crackles in order to screen pathologies and their progress during medication.

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Respiratory acoustic thoracic imaging (RATHI) permits analysing lung sounds (LS) temporal and spatial distribution, however, a deep understanding of RATHI repeatability associated with the pulmonary function is necessary. As a consequence, in the current work intrasubject variability of RATHI is evaluated at different airflows. For generating RATHIs, LS were acquired at the posterior thoracic surface. The associated image was computed at the inspiratory phases by interpolation through a Hermite function. The acoustic information of eleven subjects was considered at airflows of 1.0, 1.5 and 2.0 L/s. Several RATHIs were generated for each subject according to the number of acquired inspiratory phases. Quadratic mutual information based on Cauchy-Schwartz inequality (I(CS)) was used to evaluate the degree of similitude between intrasubject RATHIs. The results indicated that, for the same subject, I(CS) averaged 0.893, 0.897, and 0.902, for airflows of 1.0, 1.5, and 2 L/s, respectively. In addition, when the airflow was increased, increments in intensity values and in the dispersion of the spatial distribution reflected in RATHI were observed. In conclusion, since the intrasubject variability of RATHI was low for airflows between 1.0 and 2.0 L/s, the pattern of sound distribution during airflow variations is repeatable but differences in sound intensity should be considered.

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RATHI was introduced as an attempt to further improve the association between anatomical zones and specific breathing activity, both spatially and temporally. This work compares RATHI with clinical pulmonary auscultation (PA) to assess the concordance between both procedures to detect asymmetries in lung sound (LS) intensities. Twelve healthy young males participated in the study and were auscultated by two experts. RATHI consisted in the acquisition of acoustical signals with an array of 5x5 sensors, while experts auscultated and described the intensity of LS heard using the same stethoscope on each sensor's position within the array. Comparisons were established looking for intensity asymmetries between apical vs. basal pulmonary regions and right vs. left hemithorax. By RATHI, most of the subjects showed asymmetries between apical and basal regions higher than 20%, whereas between left and right hemithorax asymmetries higher than 20% occurred only half of the time. RATHI and PA agreed 83 to 100% when apical to base acoustical information was compared, but when left to right asymmetries were considered these figures were about 40 to 50%. We concluded that RATHI has advantages as it gave more detailed and measurable information on LS than clinicians, who could not detect intensity asymmetries mainly below 20%.

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Crackles sounds have been associated with several pulmonary pathologies and diverse algorithms have been proposed for extracting and counting them from the acquired lung sound. These tasks depend among other factors, of the relation between the magnitude of the crackle and the background lung sound. In this work, we explore multivariate signal processing to deal with the tasks and propose a new concept, the discontinuous adventitious sounds imaging. The image formation is founded on the results of two proposed methodologies that use an autoregressive (AR) model. In the first case, the AR coefficients feed an artificial neural network (ANN) to classify temporal acoustic information as healthy or sick and; in the second case, a time-variant AR (TVAR) model, obtained by the RLS algorithm, permits to detect changes in the TVAR coefficients to be associated with the number of crackles. For AR-ANN, the ratio of the temporal windows classified as sick to the classified as healthy is used as an index to form the adventitious image, while for TVAR-RLS, an estimation of the number of crackles is obtained to form the corresponding image. The results indicated that fine and coarse crackles could be detected and counted even with very low crackle magnitude so that the formation of a crackle distribution image was consistent.

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Several techniques have been explored to detect automatically fine and coarse crackles; however, the solution for automatic detection of crackles remains insufficient. The purpose of this work was to explore the capacity of the time-variant autoregressive (TVAR) model to detect and to provide an estimate number of fine and coarse crackles in lung sounds. Thus, simulated crackles inserted in normal lung sounds and real lung sounds containing adventitious sounds were processed with TVAR and by an expert that based crackle detection on time-expanded waveform-analysis. The coefficients of the TVAR were obtained by an adaptive filtering prediction scheme. The adaptive filter used the recursive least squares algorithm with a forgetting factor of 0.97 and the model order was four. TVAR model showed an efficiency to detect crackles over 90% even with crackles overlapping and amplitudes as low as 1.5 of the standard deviation of background lung sounds, where expert presented an efficiency around 30%. In conclusion, TVAR model is a proper alternative to detect and to provide an estimate number of fine and coarse crackles, even in presence of crackles overlapping and crackles with low amplitude, conditions where crackles detection based on time-expanded waveform-analysis reveals evident limitations.

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Sound transmission has been of interest for many years in an attempt to study the structure of the lung and different researches have shown that artificial sounds produce a lateralization of sound information at the thoracic surface. Most of these studies have use non-simultaneous recording and input sounds introduced at the mouth or other thoracic points. In this paper, we present acoustic thoracic images, for transmitted glottal sounds, formed by a multichannel system with an array of 5x5 microphones. The study was done using 4 healthy subjects and 4 subjects having diffuse interstitial pneumonia. In both groups of subjects, it was found that the thorax behaves as a lowpass filter depending on the physical properties of its components, and that the transmitted acoustic thoracic imaging (TATHI) could reflect such properties. In most of the healthy subjects right to left asymmetries and heterogeneous apical to basal distribution were found. In patients the lateral dominance was lost and an intensity increment in the frequency band of 300 to 600 Hz was revealed. We conclude that TATHI permits to observe easily the spatial extension of the disease through sound transmission.

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The mechanisms involved in the generation of heart sounds have always been of interest, mainly for diagnosis purposes. As a result, mathematical models have been proposed for first (S1) and second (S2) heart sounds and different efforts have been made to select the best signal processing tool to analyze them. Different frequency analysis techniques have been used to relate cardiac structure to the vibration they emit. In this work, we applied the empirical mode decomposition (EMD), a recently developed technique, for time-frequency (TF) analysis of heart sounds. EMD has shown interesting properties for biomedical signals related to nonlinear and non-stationary analysis. EMD is an adaptive decomposition since the extracted information is obtained directly from the signal without the use of kernels or mother waveforms. In this paper, EMD is first investigated in simulated scenarios through mathematical models for SI and S2 to validate its performance. Later, a real heart sound acquired over the thoracic surface of a healthy subject is analyzed. The work points out the advantage of EMD for this task.

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Several attempts have been made to achieve a quantitative analysis of lung sounds mainly for two purposes: a) an understanding of their genesis, and b) an insight of their changes with pathologies for medical diagnosis. Early studies involved the collection of acoustic information at several positions on the thoracic surface or at the extra-thoracic trachea with one up to four microphones, but with a non-simultaneous acquisition. However, an increment for simultaneous acquisition points has been suggested; for example, as a consequence of multichannel acquisition acoustic visualization through computerized interpolation has emerged being helpful to analyze lung sounds (LS) origin, distribution, and relation to ventilation. Nevertheless, quantitative analysis of lung sounds requires eliminating interference signals prior to the extraction of relevant features. The acquired signals not only contain LS but also heart sounds (HS) among other interferences. HS are unavoidable and sometimes represent severe disturbing interference. This paper proposes a HS cancellation scheme as an extension of a previous effort using the Empirical Mode Decomposition (EMD) and a combination of time warping with linear adaptive FIR filtering. Simulated signals are used to evaluate the performance of the proposed scheme under known and controlled scenarios.

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As respiratory sounds contain mechanical and clinical pulmonary information, technical efforts have been devoted during the past decades to analysing, processing and visualising them. The aim of this work was to evaluate deterministic interpolating functions to generate surface respiratory acoustic thoracic images (RATHIs), based on multiple acoustic sensors. Lung sounds were acquired from healthy subjects through a 5 x 5 microphone array on the anterior and posterior thoracic surfaces. The performance of five interpolating functions, including the linear, cubic spline, Hermite, Lagrange and nearest neighbour method, were evaluated to produce images of lung sound intensity during both breathing phases, at low (approximately 0.5ls(-1)) and high (approximately 1.0ls(-1)) airflows. Performance indexes included the normalised residual variance nrv (i.e. inaccuracy), the prediction covariance cv (i.e. precision), the residual covariance rcv (i.e. bias) and the maximum squared residual error semax (i.e. tolerance). Among the tested interpolating functions and in all experimental conditions, the Hermite function (nrv=0.146 +/- 0.059, cv= 0.925 +/- 0.030, rcv = -0.073 +/- 0.068, semax = 0.005 +/- 0.004) globally provided the indexes closest to the optimum, whereas the nearest neighbour (nrv=0.339 +/- 0.023, cv = 0.870 +/- 0.033, rcv= 0.298 +/- 0.032, semax = 0.007 +/- 0.005) and the Lagrange methods (nrv = 0.287 +/- 0.148, cv = 0.880 +/- 0.039, rcv = -0.524 +/- 0.135, semax = 0.007 +/- 0.0001) presented the poorest statistical measurements. It is concluded that, although deterministic interpolation functions indicate different performances among tested techniques, the Hermite interpolation function presents a more confident deterministic interpolation for depicting surface-type RATHI.

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