Cell exposure to high-voltage, short-duration electric pulses can lead to temporary formation of hydrophilic pores in the plasma membrane and an increase in the membrane's permeability which consequently increases the transmembrane transport of molecules that are otherwise unable to cross the membrane. This phenomenon, termed membrane electroporation, is currently an applicable technique in different areas such as biomedicine, biotechnology, food technology and environmental applications. Electroporation pulses are generated by pulse power generators known as electroporators and delivered to the cells (in tissue) via electrodes. The objective of this paper is to review and compare characteristics of electroporation applications and equipment described in the literature and/or present on the market. Since there are no specific standards or regulations that specifically refer to the safety of medical devices with intended medical uses for electroporation, we propose guidelines for the design of clinical electroporators and define minimal requirements for their safe and efficient use which can be incorporated within the particular standards in the future. In order to facilitate the comparison of data obtained by different research groups and to enable reproduction of results under the same conditions, we want to stress the necessity of defining the electroporator's output parameters and tolerances of electroporation parameters for electroporation-based therapies.

Automatic fall detection systems ensure that elderly people get prompt assistance after experiencing a fall. Fall detection systems based on accelerometer measurements are widely used because of their portability and low cost. However, the ability of these systems to differentiate falls from Activities of Daily Living (ADL) is still not acceptable for everyday usage at a large scale. More work is still needed to raise the performance of these systems. In our research, we explored an essential but often neglected part of accelerometer-based fall detection systems—data segmentation. The aim of our work was to explore how different configurations of windows for data segmentation affect detection accuracy of a fall detection system and to find the best-performing configuration. For this purpose, we designed a testing environment for fall detection based on a Support Vector Machine (SVM) classifier and evaluated the influence of the number and duration of segmentation windows on the overall detection accuracy. Thereby, an event-centered approach for data segmentation was used, where windows are set relative to a potential fall event detected in the input data. Fall and ADL data records from three publicly available datasets were utilized for the test. We found that a configuration of three sequential windows (pre-impact, impact, and post-impact) provided the highest detection accuracy on all three datasets. The best results were obtained when either a 0.5 s or a 1 s long impact window was used, combined with pre- and post-impact windows of 3.5 s or 3.75 s.

Higher education in Europe has passed through a very dynamic period of changes during the last ten years. Since the signing of the Bologna Declaration in 1999 by the Ministers of Education from the EU states, European higher education system has aimed toward establishing harmonized programs enabling students and teachers to extensively exchange knowledge, ideas and skills. Education in the field of Biomedical Engineering has experienced changes also because of the research and development in the field which was more intensive than in other fields. Besides research in new power sources, it is the most intensive and productive research field. Much of the development in BME education in Europe is influenced by the European research policy expressed through the 7th Framework Programme where health is the major theme. In order to foster and support the changes in the European Higher Education Area (EHEA) according to the needs of research sector and the labor market, the Tempus scheme of projects was established. Tempus scheme aims to support the modernization of higher education and create an area of co-operation in the countries surrounding the EU. Our Tempus project, CRH-BME “Curricula Reformation and Harmonization in the field of Biomedical Engineering” aims to create guidelines for updating existing curricula in the field of BME in Europe in order to meet recent and future developments in the area, address new emerging interdisciplinary domains that appear as the result of the R&D progress and respond to the BME job market demands. In this paper, some policy and economic factors affecting BME education in Europe are discussed and the results of a BME education survey we prepared within the Tempus CHR-BME project are presented. The number of BME programmes in Europe has in the last decade significantly increased and there are more BME specializations as the result of growing complexity of the research and production in the field.

In patients undergoing Coronary Artery Bypass Grafting (CABG) surgery postoperative atrial fibrillation (AF) occurs with prevalence of up to 40%. The highest incidence is between the second and third day after the operation. Following cardiac surgery AF causes various complications, hemodynamic instability, and can cause heart attack, cerebral and other thromboemolisms. AF increases morbidity, duration and expense of medical treatment. This study aims to identify patients at high risk of postoperative AF. An early prediction of AF would provide a timely prophylactic treatment and would reduce incidence of arrhythmia. Patients at low risk of postoperative AF could be excluded from the side effects of anti-arrhythmic drugs. The investigation included 50 patients in whom lead II electrocardiograms were continuously recorded for 48 hours following CABG. Univariate statistical analysis was used in the search of signal features that might predict AF. The most promising identified features were: P wave duration, RR interval duration and PQ segment level. On the basis of these a nonlinear multivariate prediction model was made deploying a Support Vector Machine (SVM) classifier. The prediction accuracy was found uprising over the time. At 48 hours following CABG; the measured best average sensitivity was 95.9% and specificity 93.4%. The positive and negative predictive accuracy were 88.9% and 98.8%, respectively and the overall accuracy was 94.6%. In regard to the prediction accuracy, the risk assessment and prediction of postoperative AF are optimal to be done in the period between 24 and 48 hours following CABG.