Patients\u27 experiences of cardiology procedures using minimal conscous sedation

Aim: The study\u27s purpose was to describe patients\u27 experiences of minimal conscious sedation during diagnostic and interventional cardiology procedures.Methods: Over a 6-week period, 119 consecutive patients (10% of annual throughput) from a major metropolitan hospital in Melbourne, Australia, were interviewed using a modified version of the American Pain Society Patient Outcome Questionnaire. Patients identified pain severity using a 10-point visual analogue scale and rated their overall comfort on a 6-point Likert scale ranging from very comfortable to very uncomfortable.Results: Patients were aged 67.6 years (standard deviation 11.1), 70.8% were male, and the mean body mass index was 27.7 (standard deviation 4.8). Patients underwent diagnostic coronary angiography (67.5%), percutaneous coronary interventions (13.3%), or combined procedures (19.2%). Most patients (65%) were comfortable in the context of low-dose conscious sedation. Slight discomfort was reported by 26% of patients; 9% reported feeling uncomfortable primarily as a result of a combination of musculoskeletal pain, angina, and vasovagal symptoms experienced during the procedure. There was significant correlation (rho = .25, P = .01) between procedure length and patients\u27 report of overall comfort, suggesting longer procedures were less comfortable for patients.Conclusions: The minimal sedation protocol was effective for the majority of patients; however, 9% of patients experienced significant discomfort related to preexisting conditions, highlighting the need for individual patient assessment before, during, and after the procedure.<br /

Automated video surveillance for preventing suicide attempts

Inmate suicide by hanging is documented as a major cause of death in prisons. Important efforts have been made to develop technological prevention tools, but the proposed solutions are mostly using cumbersome devices, in addition to their lack of generalizability. Nowadays, computer vision methods for real-time video analysis have experienced impressive progress. The recent emergence of RGB-D cameras clearly illustrates the achieved advances by offering new ways for machines to interpret human activity. There was however no significant works on exploiting this evolution, and as a result, CCTV systems used for monitoring suicidal inmates are still greatly depending on human attention and intervention. This paper proposes an intelligent video surveillance system for automated detection of suicide attempts by hanging. The proposed algorithm is able to efficiently model suicidal behavior by exploiting the depth information captured by an RGB-D camera. Activity detection is then performed by classifying visual features characterizing body joint movements. Our method demonstrated a high robustness on a challenging dataset including video sequences where suicide attempts are simulated