Güray Gürkan

Analysis of brain connectivity changes after propofol injection by generalized partial directed coherence

In this paper we present a method for the analysis of multichannel EEG by using Generalized Partial Directed Coherence (gPDC) to extract cortical connectivity changes under anesthesia. 15 channel EEG data were recorded from female subjects undergoing general anesthesia for gynecological surgery. Multivariate Autoregressive (MAR) modeling was applied to multichannel, bipolar EEG segments of awake and anesthetized states. gPDCs were calculated using the derived MAR model and averaged through EEG @a frequency band (8-14 Hz) and through a number of data segments. The gPDC calculation was performed for three different sets of bipolar EEG channel pairs each of which mainly represent a specific scalp area. To derive consistency levels of gPDC values, surrogate analysis is also performed. Using paired t-test for 12 patients, we extracted significant gPDC changes between awake and anesthetized stages for each set. Analysis revealed that during transition from awake to anesthetized stage, gPDCs of central to parietal directions were suppressed whereas gPDCs of parietal to central directions were increased. The results indicate that the proposed gPDC analysis method can be used to track the changes in brain connectivity and hence to estimate the depth of anesthesia.

Topographic and temporal spectral analysis of EEG signals during anaesthesia

In this study, we present the spatial and temporal evolution of EEG signal spectrum under anaesthesia. Studied features include SEF-90, α-β power ratios, spectral entropy that are known to be used in commercially available depth of anaesthesia monitors. As an additional and comparing feature, we also present Higuchi fractal dimension that is used for analysis of non-linear systems. By means of spatial analysis, we verified the shift of occipitally dominant alpha activity to frontal regions and demonstrated corresponding topographic plots.

Wireless 3-axis accelerometer system for measurement of structural displacement

In this study, we present the design of a 3-axis acceleration measurement system that is capable of UTC (Coordinated Universal Time) time-stamping captured from a Global Positioning System (GPS) receiver module. With this achievement, the system is able to monitor and record the 3-axis vibrations with respect to an absolute time rather than the local sensor (and computer) time. Acquired 3-axis acceleration data and UTC are transferred via Bluetooth® protocol and developed software which enables monitoring and recording of UTC and acceleration data on a PC, respectively. For verification and synchronization quality test of acceleration data, shake-table tests were conducted for simulation of structural displacement and the calculated displacements were compared with a displacement sensor of a commercially available shake table system.

Comparison of classification algorithms for EOG signals

In this paper, we present a comparison Support Vector Machine (SVM) and Artificial Neural Network (ANN) for classification of electrooculogram (EOG) signals acquired under specific eye movements. These methods that are required for an eye controlled system are compared by means of their accuracy and response time. Acquired EOG signals consist of 5 different eye movements - being horizontal (right and left), vertical (up and down) and blink. EOG signal acquisition was achieved from 20 different subjects by using two EOG channels (vertical and horizontal) and 3 element feature vectors were extracted. The first two elements of the feature vectors are the peak amplitudes of two channels whereas the third element, being our proposed parameter, is the kurtosis value of the active channel. 10 of 20 randomly selected feature vectors were used for training of the classifiers whereas the rest was used for performance tests. Offline tests yield 100 % success rate for both of the classifiers. The response times of both methods make them suitable for real-time usage.

Estimation of the parameters of the respiratory system

Resistance of the respiratory system and the lung compliance are two important respiratory parameters that are often required to be measured by the respiratory physicians. In this work, Respiratory signals (mask pressure, airway flow, and lung volume) are measured by using artificial lung simulator and mannequin head and respiratory parameters set on the simulator are estimated by the best linear unbiased estimator (BLUE). However, prior to the estimation, muscular pressure signals that symbolize the effect of the respiratory parameters on the respiratory signals are computed by using least mean square (LMS) based adaptive noise canceler (ANC). It is found that LMS filter length considerably effects the filter output and in turn the estimation results. Thus, It is suggested to use misadjustment criterion in LMS-ANC filter to select the filter order by processing the signals that have only one respiratory parameter variation. In conclusion, respiratory parameters are successfully estimated from the muscular pressure signals that are filtered out with appropriate filter lengths.