The purpose of the present paper is to evaluate the sensitivity of modern lighting technologies to different types of RVCs. In order to do that, 27 modern lamps—mainly LED—have been subjected to real RVCs and their response has been assessed. The detection of RVCs on the grid has been performed according to the IEC 61000-4-30 detection method, while the response of the lamps has been measured with a light flickermeter and characterized using the instantaneous flicker perception, as defined in IEC 61000-4-15. The obtained results show a high dispersion in the response of the modern lighting technologies and high values of flicker perception, although with a lower sensitivity than the incandescent lamp. The results led the authors to propose the definition of a new immunity test to be added to the lamp immunity protocol IEC TR-61547-1, to ensure that newly produced lamps cause limited irritation to grid users.
BackgroundThe use of real-time feedback systems to guide rescuers during cardiopulmonary resuscitation (CPR) significantly contributes to improve adherence to published resuscitation guidelines. Recently, we designed a novel method for computing depth and rate of chest compressions relying solely on the spectral analysis of chest acceleration. That method was extensively tested in a simulated manikin scenario. The purpose of this study is to report the results of this method as tested in human out-of-hospital cardiac arrest (OHCA) cases.Materials and methodsThe algorithm was evaluated retrospectively with seventy five OHCA episodes recorded by monitor-defibrillators equipped with a CPR feedback device. The acceleration signal and the compression signal computed by the CPR feedback device were stored in each episode. The algorithm was continuously applied to the acceleration signals. The depth and rate values estimated every 2-s from the acceleration data were compared to the reference values obtained from the compression signal. The performance of the algorithm was assesed in terms of the sensitivity and positive predictive value (PPV) for detecting compressions and in terms of its accuracy through the analysis of measurement error.ResultsThe algorithm reported a global sensitivity and PPV of 99.98% and 99.79%, respectively. The median (P75) unsigned error in depth and rate was 0.9 (1.7) mm and 1.0 (1.7) cpm, respectively. In 95% of the analyzed 2-s windows the error was below 3.5 mm and 3.1 cpm, respectively.ConclusionsThe CPR feedback algorithm proved to be reliable and accurate when tested retrospectively with human OHCA episodes. A new CPR feedback device based on this algorithm could be helpful in the resuscitation field.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.