Tomás Aljama-Corrales

Respiratory acoustic thoracic imaging (RATHI): Assessing deterministic interpolation techniques

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 5×5 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 (∼0.5ls−1) and high (∼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 closert 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.

Assessment of multichannel lung sounds parameterization for two-class classification in interstitial lung disease patients

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.

Analysis of discontinuous adventitious lung sounds by Hilbert-Huang spectrum

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.

Assessment of time-frequency representation techniques for thoracic sounds analysis

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.

Men and women should be separately investigated in studies of orthostatic challenge due to different gender-related dynamics of autonomic response.

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.

Acoustic Thoracic Images for Transmitted Glottal Sounds

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 5times5 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.

Crackles detection using a time-variant autoregressive model

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.

Asymmetry in lung sound intensities detected by respiratory acoustic thoracic imaging (RATHI) and clinical pulmonary auscultation

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 5×5 sensors, while experts auscultated and described the intensity of LS heard using the same stethoscope on each sensors 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%.

Orthostatic stress causes immediately increased blood pressure variability in women with vasovagal syncope

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.

Adventitious lung sounds imaging by ICA-TVAR scheme

Adventitious lung sounds (ALS) as crackles and wheezes are present in different lung alterations and their automated characterization and recognition have become relevant. In fact, recently their 2D spatial distribution (SD) imaging has been proposed to help diagnose of pulmonary diseases. In this work, independent component analysis (ICA) by infomax was used to find crackles sources and from them to apply a time variant autoregressive model (TVAR) to count and imaging the ALS. The proposed methodology was assessed on multichannel LS recordings by embedding simulated fine crackles with known SD in recorded normal breathing sounds. Afterwards, the adventitious image of two patients with fibrosis and emphysema were obtained and contrasted with the classical pulmonary auscultation provided by a pneumologist. The results showed that combining ICA and TVAR leads to a robust methodology to imaging ALS.