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.

Assessment of ICA algorithms for the analysis of crackles sounds.

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.

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.

Baroreflex sensitivity assessment and heart rate variability: relation to maneuver and technique.

In the present study, we examined two baroreflex sensitivity (BRS) issues that remain uncertain: the differences among diverse BRS assessment techniques and the association between BRS and vagal outflow. Accordingly, the electrocardiogram and non-invasive arterial pressure were recorded in 27 healthy subjects, during supine with and without controlled breathing, standing, exercise, and recovery conditions. Vagal outflow was estimated by heart rate variability indexes, whereas BRS was computed by alpha-coefficient, transfer function, complex demodulation in low- and high-frequency bands, and by sequence technique. Our results indicated that only supine maneuvers showed significantly greater BRS values over the high frequency than in the low-frequency band. For maneuvers at the same frequency region, supine conditions presented a larger number of significant differences among techniques. The plots between BRS and vagal measures depicted a funnel-shaped relationship with significant log-log correlations (r=0.880-0.958). Very short latencies between systolic pressure and RR interval series in high-frequency band and strong log-log correlations between frequency bands were found. Higher variability among different baroreflex measurements was associated with higher level of vagal outflow. Methodological assumptions for each technique seem affected by non-baroreflex variation sources, and a modified responsiveness of vagal motoneurons due to distinct stimulation levels for each maneuver was suggested. Thus, highest vagal outflows corresponded to greatest BRS values, with maximum respiratory effect for the high-frequency band values. In conclusion, BRS values and differences across the tested techniques were strongly related to the vagal outflow induced by the maneuvers.