Detection of myocardial scar from the VCG using a supervised learning approach.

This paper addresses the possibility of detecting presence of scar tissue in the myocardium through the investigation of vectorcardiogram (VCG) characteristics. Scarred myocardium is the result of myocardial infarction (MI) due to ischemia and creates a substrate for the manifestation of fatal arrhythmias. Our efforts are focused on the development of a classification scheme for the early screening of patients for the presence of scar. More specifically, a supervised learning model based on the extracted VCG features is proposed and validated through comprehensive testing analysis. The achieved accuracy of 82.36% (sensitivity 84.31%, specificity 77.36%) indicates the potential of the proposed screening mechanism for detecting the presence/absence of scar tissue.

On the Detection of Myocadial Scar Based on ECG/VCG Analysis.

In this paper, we address the problem of detecting the presence of a myocardial scar from the standard electrocardiogram (ECG)/vectorcardiogram (VCG) recordings, giving effort to develop a screening system for the early detection of the scar in the point-of-care. Based on the pathophysiological implications of scarred myocardium, which results in disordered electrical conduction, we have implemented four distinct ECG signal processing methodologies in order to obtain a set of features that can capture the presence of the myocardial scar. Two of these methodologies are: 1) the use of a template ECG heartbeat, from records with scar absence coupled with wavelet coherence analysis and 2) the utilization of the VCG are novel approaches for detecting scar presence. Following, the pool of extracted features is utilized to formulate a support vector machine classification model through supervised learning. Feature selection is also employed to remove redundant features and maximize the classifier's performance. The classification experiments using 260 records from three different databases reveal that the proposed system achieves 89.22% accuracy when applying tenfold cross validation, and 82.07% success rate when testing it on databases with different inherent characteristics with similar levels of sensitivity (76%) and specificity (87.5%).

Device-associated nosocomial infection rates in intensive care units in Greece.

Site-specific, risk-adjusted incidence rates of intensive care unit (ICU)-acquired infections were obtained through standardized surveillance in 8 ICUs in Greece. High rates were observed for central line-associated bloodstream infection (12.1 infections per 1,000 device-days) and ventilator-associated pneumonia (12.5 infections per 1,000 device-days). Gram-negative microorganisms accounted for 60.4% of the isolates recovered, and Acinetobacter species were predominant. To reduce infection rates in Greek ICUs, comprehensive infection control programs are required.

Epidemiology and molecular analysis of intestinal colonization by vancomycin-resistant enterococci in greek hospitals.

From 1,246 specimens collected from 13 Greek hospitals, 266 vancomycin-resistant enterococci strains were isolated from 255 patients (20.5%). The VanA phenotype was present in 82 (30.8%) strains, the VanB phenotype in 17 (6.4%) strains, the VanC1 phenotype in 152 (57.1%) strains, and the VanC2/C3 phenotypes in 15 (5.6%) strains. When only VanA and VanB phenotypes were considered, the overall prevalence was 7.5%. Eighty-six isolates exhibiting the VanA or VanB phenotype were analyzed by pulsed-field gel electrophoresis (PFGE), and 46 PFGE groups were found.