Background: Breath-hold diving (BH-diving) is associated to extreme environmental conditions, prolonged physical activity, and complex adaptation mechanisms to supply enough O2 to vital organs. Consequently, one of the biggest effects could be an increased exercise-induced muscle fatigue, in both skeletal and cardiac muscles that can induce an increase of muscles injury markers including creatine kinase (CK), aspartate transferase (AST), and alanine transferase (ALT) when concerning the skeletal muscle, cardiac creatine kinase isoenzyme (CK-MBm) and cardiac troponin I (cTnI) when concerning the cardiac muscle, and lactate dehydrogenase (LDH) as index of muscle stress. The aim of this study is to investigate serum cardiac and skeletal muscle markers before and after a BH-diving training session. Results: We found statistically significant increases of CK (T0: 136.1% p < 0.0001; T1: 138.5%, p < 0.0001), CK-MBm (T0: 145.1%, p < 0.0001; T1: 153.2%, p < 0.0001) LDH (T0: 110.4%, p < 0.0003; T1: 110.1%, p < 0.0013) in both T0 and T1 blood samples, as compared to basal value. AST showed a statistically significant increase only at T0 (106.8%, p < 0.0007) while ALT did not exhibit statistically significant changes. We did not find any changes in cTnI levels between pre-dive and post-dive samples.

Purpose: Deep diving using mixed gas with closed-circuit rebreathers (CCRs) is increasingly common. However, data regarding the effects of these dives are still scarce. This preliminary field study aimed at evaluating the acute effects of deep (90-120 msw) mixed-gas CCR bounce dives on lung function in relation with other physiological parameters. Methods: Seven divers performed a total of sixteen open-sea CCR dives breathing gas mixture of helium, nitrogen and oxygen (trimix) within four days at 2 depths (90 and 120 msw). Spirometric parameters, SpO2, body mass, hematocrit, short term heart rate variability (HRV) and critical flicker fusion frequency (CFFF) were measured at rest 60 min before the dive and 120 min after surfacing.

In spite of many modifications to decompression algorithms, the incidence of decompression sickness (DCS) in scuba divers has changed very little. The success of stage, compared to linear ascents, is well described yet theoretical changes in decompression ratios have diminished the importance of fast tissue gas tensions as critical for bubble generation. The most serious signs and symptoms of DCS involve the spinal cord, with a tissue half time of only 12.5 minutes. It is proposed that present decompression schedules do not permit sufficient gas elimination from such fast tissues, resulting in bubble formation. Further, it is hypothesized that introduction of a deep stop will significantly reduce fast tissue bubble formation and neurological DCS risk. A total of 181 dives were made to 82 fsw (25 m) by 22 volunteers. Two dives of 25 min and 20 min were made, with a 3 hr 30 min surface interval and according to 8 different ascent protocols. Ascent rates of 10, 33 or 60 fsw/min (3, 10, 18 m/min) were combined with no stops or a shallow stop at 20 fsw (6 m) or a deep stop at 50 fsw (15 m) and a shallow at 20 fsw (6 m). The highest bubbles scores (8.78/9.97), using the Spencer Scale (SS) and Extended Spencer Scale (ESS) respectively, were with the slowest ascent rate. This also showed the highest 5 min and 10 min tissue loads of 48% and 75%. The lowest bubble scores (1.79/2.50) were with an ascent rate of 33 fsw (10 m/min) and stops for 5 min at 50 fsw (15 m) and 20 fsw (6 m). This also showed the lowest 5 and 10 min tissue loads at 25% and 52% respectively. Thus, introduction of a deep stop significantly reduced Doppler detected bubbles together with tissue gas tensions in the 5 and 10 min tissues, which has implications for reducing the incidence of neurological DCS in divers.

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.