Abstract
There are several electronic stethoscopes available on the market today, with a very high potential for healthcare namely telemedicine, assisted decision and education. However, there are no recent comparatives studies published about the recording quality of auscultation sounds. In this study we aim to: a) define a ranking, according to experts opinion of 6 of the most relevant electronic stethoscopes on the market today; b) verify if there are any relations between a stethoscope's performance and the type of pathology present; c) analyze if some pathologies are more easily identified than others when using electronic auscultation. Our methodology consisted in creating two study groups: the first group included 18 cardiologists and cardiology house officers, acting as the gold standard of this work. The second included 30 medical students. Using a database of heart sounds recorded in real hospital environments, we applied questionnaires to observers from each group. The first group listened to 60 cardiac auscultations recorded by the 6 stethoscopes, and each one was asked to identify the pathological sound present: aortic stenosis, mitral regurgitation or normal. The second group was asked to choose, between two auscultation recordings, using as criteria the best sound quality for the identification of pathological sounds. Results include a total of 1080 evaluations, in which 72% of cases were correctly diagnosed. A detailed breakdown of these results is presented in this paper. As conclusions, results showed that the impact of the differences between stethoscopes is very small, given that we did not find statistically significant differences between all pairs of stethoscopes. Normal sounds showed to be easier to identify than pathological sounds, but we did not find differences between stethoscopes in this identification.

Abstract
Rheumatoid arthritis (RA) is a chronic inflammatory disease that primarily affects the small joints of the hand. High frequency ultrasound imaging is used to measure the inflammatory activity in the joint capsule region of Metacarpophalangeal (MCP) joint. In our previous work, the problem of bones and joint capsule segmentation was addressed and in this work we aim to automatically identify the tendon using previously segmented structures. The extensor tendon is located above the metacarpal and phalange bone and the joint capsule. Tendon and bursal involvement are frequent and often clinically dominant in early RA. Ridge-like structures are enhanced and pre-processed to reduce speckle noise using a Log-Gabor filter. These regions are then simplified using medial axis transform and vertically connected lines are removed. Adjacent lines are connected using morphological operators and short lines are filtered by thresholding. Physiological information is used to create a distance map for all the lines using prior knowledge of the bone and capsule region location. Based on this distance map, the tendon is finally segmented and its shape refined by using active contours. The segmentation algorithm was tested on 90 images and experimental results demonstrate the accuracy of the proposed algorithm. The automatic segmentation was compared with an expert manual segmentation, and a mean error of 3.7 pixels and a standard deviation of 2 pixels were achieved, which are interested results for integration into future computer-assisted decision systems.

Abstract
The first step in any non linear time series analysis, is to characterize signals in terms of periodicity, stationarity, linearity and predictability. In this work we aim to find if PCG (phonocardiogram) and ECG (electrocardiogram) time series are generated by a deterministic system and not from a random stochastic process. If PCG and ECG are non-linear deterministic systems and they are not very contaminated with noise, data should be confined to a finite dimensional manifold, which means there are structures hidden under the signal that could be used to increase our knowledge in forecasting future values of the time series. A non-linear process can give rise to very complex dynamic behaviours, even though the underlying process is purely deterministic and probably low-dimensional. To test this hypothesis, we have generated 99 surrogates and then we compared the fitting capability of AR (auto-regressive) models on the original and surrogate data. The results show with a 99\% of confidence level that PCG and ECG were generated by a deterministic process. We compared the fitting capability of an ECG and PCG to AR linear models, using a multi-channel approach. We make an assumption that if a signal is more linearly predictable than another one, it may adjust better to these AR linear models. The results showed that ECG is more linearly predictable (for both channels) than PCG, although a filtering step is needed for the first channel. Finally we show that the false nearest neighbour method is insufficient to identify the correct dimension of the attractor in the reconstructed state space for both PCG and ECG signals.

Abstract
Cardiovascular variability and homeostasis control in response to precise noxious stimuli and different analgesic doses were analyzed. Cardiovascular variability was altered in response to noxious stimulation, and amplitude responses varied in a dose-dependent manner with the analgesic. Responses were more pronounced to laringoscopy/intubation, when compared to tetanic and incision stimuli. Homeostasis control was also altered in response to stimulation, demonstrating that dynamic cardiovascular control relations are modified in response to precise noxious stimuli and anesthetic drugs.

Abstract
Auscultation is a routine exam and the first line of screening in heart pathologies. The objective of this study was to assess if using a new data collection system, the DigiScope Collector, with a guided and automatic annotation of heart auscultation, different levels of expertise/experience users could collect similar digital auscultations. Data were collected within the Heart Caravan Initiative (Paraiba, Brasil). Patients were divided into two study groups: Group 1 evaluated by a third year medical student (User 1), and an experienced nurse (User 2); Group 2 evaluated by User 2 and an Information Technology professional (User 3). Patients were auscultated sequentially by the two users, according to the randomization. Features extracted from each data set included the length (HR) of the audio files, the number of repetitions per auscultation area, heart rate, first (S1) and second (S2) heart sound amplitudes, S2/S1, and aortic (A2) and pulmonary (P2) components of the second heart sound and relative amplitudes (P2/A2). Features extracted were compared between users using paired-sample test Wilcoxon test, and Spearman correlations (P < 0.05 considered significant). Twenty-seven patients were included in the study (13 Group 1, and 14 Group 2). No statistical significant differences were found between groups, except in the time of auscultation (User 2 consistently presented longer auscultation time). Correlation analysis showed significant correlations between extracted features from both groups: S2/S1 in Group 1, and S1, S2, A2, P2, P2/A2 amplitudes, and HR in Group 2. Using the DigiScope Collector, we were able to collect similar digital auscultations, according to the features evaluated. This may indicate that in sites with limited access to specialized clinical care, auscultation files may be acquired and used in telemedicine for an expert evaluation.

Abstract
In this work, we present a method to extract features from heart sound signals in order to enhance segmentation performance. The approach is data-driven, since the way features are extracted from the recorded signals is adapted to the data itself. The proposed method is based on the extraction of delay vectors, which are modeled with Gaussian mixture model priors, and an information-theoretic dimensionality reduction step which aims to maximize discrimination between delay vectors in different segments of the heart sound signal. We test our approach with heart sounds from the publicly available PhysioNet dataset showing an average F-1 score of 92.6% in detecting S-1 and S-2 sounds.