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Introduction: Nowadays, more than 10,000 different types of medical devices can be found in hospitals. This way, medical electrical equipment is being employed in a wide variety of fields in medical sciences with different physiological effects and measurements. Hospitals and medical centers must ensure that their critical medical devices are safe, accurate, reliable and operational at the required level of performance. Defibrillators are critical resuscitation devices. The use of reliable defibirillators has led to more effective treatments and improved patient safety through better control and management of complications during Cardiopulmonary Resuscitation (CPR). Materials and Methods: The metrological reliability of twenty frequent use, manual defibrillators in use ten hospitals (4 private and 6 public) in one of the provinces of Iran according to international and national standards was evaluated. Results: Quantitative analysis of control and instrument accuracy showed the amount of the obtained results in many units are critical which had less value over the standard limitations especially in devices with poor battery. For the accuracy of delivered energy analysis, only twelve units delivered acceptable output values and the precision in the output energy measurements especialy in weak battry condition, after activation of discharge alarm, were low. Conclusion: Obtained results indicate a need for new and severe regulations on periodic performance verifications and medical equipment quality control program especially for high risk instruments. It is also necessary to provide training courses on the fundumentals of operation and performane parameters for medical staff in the field of meterology in medicine and how one can get good accuracy results especially in high risk medical devices.

Introduction: Modern medicine employs a wide variety of instruments with different physiological effects and measurements. Periodic verifications are routinely used in legal metrology for industrial measuring instruments. The correct operation of electrosurgical generators is essential to ensure patient’s safety and management of the risks associated with the use of high and low frequency electrical currents on human body. Material and Methods: The metrological reliability of 20 electrosurgical equipment in six hospitals (3 private and 3 public) was evaluated in one of the provinces of Iran according to international and national standards. Results: The achieved results show that HF leakage current of ground-referenced generators are more than isolated generators and the power analysis of only eight units delivered acceptable output values and the precision in the output power measurements was low. Conclusion: Results indicate a need for new and severe regulations on periodic performance verifications and medical equipment quality control program especially in high risk instruments. It is also necessary to provide training courses for operating staff in the field of meterology in medicine to be acquianted with critical parameters to get accuracy results with operation room equipment.

Background: Brain tissue segmentation for delineation of 3D anatomical structures from magnetic resonance (MR) images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), but only if the obtained segmentation results are correct. Due to image artifacts such as noise, low contrast and intensity non-uniformity, there are some classification errors in the results of image segmentation. Objective: An automated algorithm based on multi-layer perceptron neural networks (MLPNN) is presented for segmenting MR images. The system is to identify two tissues of WM and GM in human brain 2D structural MR images. A given 2D image is processed to enhance image intensity and to remove extra cerebral tissue. Thereafter, each pixel of the image under study is represented using 13 features (8 statistical and 5 non- statistical features) and is classified using a MLPNN into one of the three classes WM and GM or unknown. Results: The developed MR image segmentation algorithm was evaluated using 20 real images. Training using only one image, the system showed robust performance when tested using the remaining 19 images. The average Jaccard similarity index and Dice similarity metric for the GM and WM tissues were estimated to be 75.7 %, 86.0% for GM, and 67.8% and 80.7%for WM, respectively. Conclusion: The obtained performances are encouraging and show that the presented method may assist with segmentation of 2D MR images especially where categorizing WM and GM is of interest. [ABSTRACT FROM AUTHOR]

BCI is one of the most intriguing technologies among other HCI systems, mostly because of its capability of recording brain activities. Spelling BCIs, which help paralyzed people to maintain communication, are one of the striking topics in the field of BCI. In this scientific a spelling BCI system with high transfer rate and accuracy that uses SSVEP signals is proposed. In addition, we suggested that LED light sources can provide proper signals for speller BCIs and they can be used in future. [ABSTRACT FROM AUTHOR]

Background: Electromyographic (EMG) signals obtained from a contracted muscle contain valuable information on its activity and health status. Much of this information lies in motor unit potentials (MUPs) of its motor units (MUs), collected during the muscle contraction. Hence, accurate estimation of a MUP template for each MU is crucial., Objective: To investigate the possibility of improving MUP template estimation using the wavelet denoising technique., Material and Methods: In this analytical study, several MUP template estimators were developed by combining conventional estimation methods and wavelet denoising techniques. A MUP template was initially estimated using conventional methods such as mean, median, median-trimmed mean, or mode. Thereafter, it was post-processed using the wavelet denoising technique., Results: Evaluation results of the studied estimators using 40 simulated EMG signals with a true template for each constituent MUP train showed that augmented wavelet- based template estimation methods are more reliable than conventional methods. However, on average, wavelet denoising was not much effective. Around 40 MUPs of a MU is sufficient to estimate its MUP template., Conclusions: Although wavelet techniques are effective in EMG signal analysis, here wavelet denoising did not practically improve MUP template estimation. Considering computational simplicity and estimation error, the two methods median and median-trimmed mean are practical estimators that can provide a good estimation of a MUP template for a MU when approximately 40 MUPs are available. Nevertheless, the baseline noise level in the MUP templates estimated using the median-trimmed mean method is slightly lower than that in the templates estimated using the median method., (Copyright: © Journal of Biomedical Physics and Engineering.)