Humidity and measurement of volatile propofol using MCC-IMS (EDMON)

The bedside Exhaled Drug MONitor – EDMON measures exhaled propofol in ppbv every minute based on multi-capillary column – ion mobility spectrometry (MCC-IMS). The MCC pre-separates gas samples, thereby reducing the infuence of the high humidity in human breath. However, preliminary analyses identifed substantial measurement deviations between dry and humid calibration standards. We therefore performed an analytical validation of the EDMON to evaluate the infuence of humidity on measurement performance. A calibration gas generator was used to generate gaseous propofol standards measured by an EDMON device to assess linearity, precision, carry-over, resolution, and the infuence of diferent levels of humidity at 100% and 1.7% (without additional) relative humidity (reference temperature: 37°C). EDMON measurements were roughly half the actual concentration without additional humidity and roughly halved again at 100% relative humidity. Standard concentrations and EDMON values correlated linearly at 100% relative humidity (R²=0.97). The measured values were stable over 100min with a variance≤10% in over 96% of the measurements. Carry-over efects were low with 5% at 100% relative humidity after 5min of equilibration. EDMON measurement resolution at 100% relative humidity was 0.4 and 0.6 ppbv for standard concentrations of 3 ppbv and 41 ppbv. The infuence of humidity on measurement performance was best described by a second-order polynomial function (R²≥0.99) with infuence reaching a maximum at about 70% relative humidity. We conclude that EDMON measurements are strongly infuenced by humidity and should therefore be corrected for sample humidity to obtain accurate estimates of exhaled propofol concentrations

Classification of EEG Based BCI Signals Imagined Hand Closing and Opening

40th International Conference on Telecommunications and Signal Processing (TSP) -- JUL 05-07, 2017 -- Barcelona, SPAINYavuz, Ebru Nur Vanli/0000-0001-6915-7493WOS: 000425229000094Brain-computer interfaces allow people to manage electronic devices such as computers without using their motor nervous system. When the brain is in a function, nerve cells in the brain communicate with each other with electrochemical interactions. Electroencephalogram (EEG) signals are recorded with the aid of electrodes during this function of the brain. These signals enable interaction between people and electronic devices. This interaction forms the basis of brain computer interface (BCI) systems which facilitates lives of paralyzed patients who do not have any problems with their cognitive functioning. Therefore, for high-performance BCI systems, pre-processing technique and classification method applied to these signals and features extracted from these signals are crucial. in this study, we studied a new EEG data set recorded from 29 people during imagination of hand opening/closing movement. While moving average filter was used a pre-processing technique, the features were extracted by Hilbert Transform and Mean Derivative. Afterwards, extracted features were classified by k-nearest neighbor method. Average classification accuracy (CA) with pre-processing was achieved 82.23%, which was 12.78% higher than the average CA obtained by unprocessed EEG data set and 16.63% greater than the previous works reported in the literature. the achieved results showed that the proposed method has a great potential to be applied general with a highperformance in general