2D echocardiography which is the golden standard in clinics becomes the new trend of analysis in diving via its high advantages in portability for diagnosis. By the way, the major weakness of this system is non-integrated analysis platform for bubble recognition. In this study, we developed a full automatic method to recognize bubbles in videos. Gabor Wavelet based neural networks are commonly used in face recognition and biometrics. We adopted a similar approach to overcome recognition problem by training our system through real bubble morphologies. Our method does not require a segmentation step which is almost crucial in several studies. Our correct detection rate varies between 82.7-94.3%. After the detection, we classified our findings on ventricles and atria using fuzzy k-means algorithm. Bubbles are clustered in three different subjects with 84.3-93.7% accuracy rates. We suggest that this routine would be useful in longitudinal analysis and subjects with congenital risk factors

The use of precordial Doppler monitoring to prevent decompression sickness (DS) is well known by the scientific community as an important instrument for early diagnosis of DS. However, the timely and correct diagnosis of DS without assistance from diving medical specialists is unreliable. Thus, a common protocol for the manual annotation of echo Doppler signals and a tool for their automated recording and annotation are necessary. We have implemented original software for efficient bubble appearance annotation and proposed a unified annotation protocol. The tool auto-sets the response time of human “bubble examiners,” performs playback of the Doppler file by rendering it independent of the specific audio player, and enables the annotation of individual bubbles or multiple bubbles known as “showers.” The tool provides a report with an optimized data structure and estimates the embolic risk level according to the Extended Spencer Scale. The tool is built in accordance with ISO/IEC 9126 on software quality and has been projected and tested with assistance from the Divers Alert Network (DAN) Europe Foundation, which employs this tool for its diving data acquisition campaigns.

Introduction: Nitric oxide (NO) plays an important role in the physiology and pathophysiology of diving, and the related endothelial dysfunction and oxidative stress roles have been extensively investigated. However, most available data have been obtained before and after the dive, whilst, as far as we know, no data is available about what happens during the water immersion phase of dive. The scope of this study is to investigate the Nitrate and Nitrite (NOX) concentration and the total plasma antioxidant capacity (TAC) before, during and after a single SCUBA dive in healthy scuba diving volunteers, as well as to look for evidence of a possible relationship with venous gas bubble formation. Materials and Methods: Plasma, obtained from blood of 15 expert SCUBA divers, 13 male and 2 female, was investigated for differences in NOX and TAC values in different dive times. Differences in NOX and TAC values in subjects previously known as "bubble resistant" (non-bubblers - NB) and "bubble prone" (Bubblers - B) were investigated.

Divers’ health state after underwater activity can be assessed after the immersion using precordial echo Doppler examination. An audio analysis of the acquired signals is performed by specialist doctors to detect circulating gas bubbles in the vascular system and to evaluate the decompression sickness risk. Since on-site medical assistance cannot always be guaranteed, we propose a system for automatic emboli detection using a custom portable device connected to the echo Doppler instrument. The empirical mode decomposition method is used to develop a real-time algorithm able to automatically detect embolic events and, consequently, assess the decompression sickness risk according to the Spencer’s scale. The proposed algorithm has been tested according to an experimental protocol approved by the Divers Alert Network. It involved 30 volunteer divers and produced 37 echo Doppler files useful for the algorithm’s performances evaluation. The results obtained by the proposed emboli detection algorithm (83% sensitivity and 76% specificity) make the system particularly suitable for real-time evaluation of the decompression sickness risk level. Furthermore, the system could also be used in continuous monitoring of hospitalized patients with embolic risks such as post surgery ones.

The purpose of this study was to analyze the correlation between decompression-related physiological stress markers, given by inflammatory processes and immune system activation and changes in Heart Rate Variability, evaluating whether Heart Rate Variability can be used to estimate the physiological stress caused by the exposure to hyperbaric environments and subsequent decompression. A total of 28 volunteers participated in the experimental protocol. Electrocardiograms were performed; blood samples were obtained for the quantification of red cells, hemoglobin, hematocrit, neutrophils, lymphocytes, platelets, aspartate transaminase (AST), alanine aminotransferase (ALT), and for immunophenotyping and microparticles (MP) research through Flow Cytometry, before and after each experimental protocol from each volunteer.