In this study, a novel approach is proposed, the imaging of crackle sounds distribution on the thorax based on processing techniques that could contend with the detection and count of crackles; hence, the normalized fractal dimension (NFD), the univariate AR modeling combined with a supervised neural network (UAR-SNN), and the time-variant autoregressive (TVAR) model were assessed. The proposed processing schemes were tested inserting simulated crackles in normal lung sounds acquired by a multichannel system on the posterior thoracic surface. In order to evaluate the robustness of the processing schemes, different scenarios were created by manipulating the number of crackles, the type of crackles, the spatial distribution, and the signal to noise ratio (SNR) at different pulmonary regions. The results indicate that TVAR scheme showed the best performance, compared with NFD and UAR-SNN schemes, for detecting and counting simulated crackles with an average specificity very close to 100%, and average sensitivity of 98 ± 7.5% even with overlapped crackles and with SNR corresponding to a scaling factor as low as 1.5. Finally, the performance of the TVAR scheme was tested against a human expert using simulated and real acoustic information. We conclude that a confident image of crackle sounds distribution by crackles counting using TVAR on the thoracic surface is thoroughly possible. The crackles imaging might represent an aid to the clinical evaluation of pulmonary diseases that produce this sort of adventitious discontinuous lung sounds.

The aim of this paper is to compare baroreflex sensitivity (BRS) following anesthesia induction via propofol to pre-induction baseline values through a systematic and mathematically robust analysis. Several mathematical methods for BRS quantification were applied to pre-operative and intra-operative data collected from patients undergoing major surgery, in order to track the trend in BRS variations following anesthesia induction, as well as following the onset of mechanical ventilation. Finally, a comparison of BRS trends in chronic hypertensive patients (CH) with respect to non hypertensive (NH) patients was performed. 10 NH and 7 CH patients undergoing major surgery with American Society of Anesthesiologists classification score 2.5 ± 0.5 and 2.6 ± 0.5 respectively, were enrolled in the study. A Granger causality test was carried out to verify the causal relationship between RR interval duration and systolic blood pressure (SBP), and four different mathematical methods were used to estimate the BRS: (1) ratio between autospectra of RR and SBP, (2) transfer function, (3) sequence method and (4) bivariate closed loop model. Three different surgical epochs were considered: baseline, anesthetic procedure and post-intubation. In NH patients, propofol administration caused a decrease in arterial blood pressure (ABP), due to its vasodilatory effects, and a reduction of BRS, while heart rate (HR) remained unaltered with respect to baseline values before induction. A larger decrease in ABP was observed in CH patients when compared to NH patients, whereas HR remained unaltered and BRS was found to be lower than in the NH group at baseline, with no significant changes in the following epochs when compared to baseline. To our knowledge, this is the first study in which the autonomic response to propofol induction in CH and NH patients was compared. The analysis of BRS through a mathematically rigorous procedure in the perioperative period could result in the availability of additional information to guide therapy and anesthesia in uncontrolled hypertensive patients, which are prone to a higher rate of hypotension events occurring during general anesthesia induction.

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 region 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 quantitativeestimation of fibrosis region.

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Raman spectroscopy of biological samples presents undesirable noise and fluorescence generated by the biomolecular excitation. The reduction of these types of noise is a fundamental task to obtain the valuable information of the sample under analysis. This paper proposes the application of the empirical mode decomposition (EMD) for noise elimination. EMD is a parameter-free and adaptive signal processing method useful for the analysis of nonstationary signals. EMD performance was compared with the commonly used Vancouver algorithm (VRA) through artificial data (Teflon), synthetic (Vitamin E and paracetamol) and biological (Mouse brain and human nails) Raman spectra. The correlation coefficient ([Formula: see text]) was used as performance measure. Results on synthetic data showed a better performance of EMD ([Formula: see text]) at high noise levels compared with VRA ([Formula: see text]). The methods with simulated fluorescence added to artificial material exhibited a similar shape of fluorescence in both cases ([Formula: see text] for VRA and [Formula: see text] for EMD). For synthetic data, Raman spectra of vitamin E were used and the results showed a good performance comparing both methods ([Formula: see text] for EMD and [Formula: see text] for VRA). Finally, in biological data, EMD and VRA displayed a similar behavior ([Formula: see text] for EMD and [Formula: see text] for VRA), but with the advantage that EMD maintains small amplitude Raman peaks. The results suggest that EMD could be an effective method for denoising biological Raman spectra, EMD is able to retain information and correctly eliminates the fluorescence without parameter tuning.