Sharif Digital Repository

In this paper, effects of general anesthesia on the electroencephalogram (EEC) has been modeled with an enhanced physiological mean field theory of electrocortical activity. Enhancement is done by inserting two intrinsic ion channels (IKNa and IAR) in Liley's mean field model. In addition to excitatory and inhibitory synapses, intrinsic ion channels can generate or manipulate the brain rhythms. IKNa and IAR can produce slow brain rhythms (delta band frequency) in deep levels of anesthesia. We represent the activities of each mentioned ion channels by cascading a nonlinear function and a first order low pass filter. Linearized and numerical solutions of the modified model show that the power... 

Sleep spindle is the hallmark of second stage of sleep in human being, which is defined as a rhythmic sequence with waxing and waning waves, whose frequency is approximately between 8 to 14 Hz, and its time duration is between 0.5 to 2 seconds. Bump modeling is a method for extracting regions with higher amounts of energy in a related time-frequency map. The bump model of the sleep spindle consists of a group of high energy bumps concentrating in approximately 8 to 14 Hz frequency band. In this study, it will be shown that the power of bumps of EEG can be used in automated detection of sleep spindle. The presented method sensitivity is 99.41% which shows high correctly detection rate, and... 

In this paper, an enhanced local mean-field model that is suitable for simulating the electroencephalogram (EEG) in different depths of anesthesia is presented. The main building elements of the model (e.g., excitatory and inhibitory populations) are taken from Steyn-Ross and Bojak and Liley mean-field models and a new slow ionic mechanism is included in the main model. Generally, in mean-field models, some sigmoid-shape functions determine firing rates of neural populations according to their mean membrane potentials. In the enhanced model, the sigmoid function corresponding to excitatory population is redefined to be also a function of the slow ionic mechanism. This modification adapts the... 

In this article we investigate on the evolution of EEG spectra over different depth of anesthesia from deep to very light anesthesia where immediately followed by waking. Low frequency components of EEG spectra (delta band) are examined using two different methods. One is based on Fourier transform of the low pass filtered EEG and the other is based on extraction of a kind of negative peak slow wave activity (SWA) and estimating its Fourier transform. Results show that in transition from deep to light anesthesia, not all energies of delta frequencies are decreased. There are some particular frequencies that (e.g. ~0.7 Hz) their power may even be increased by reduction of anesthetic drug... 

In this paper we investigate the modulation properties of high frequency EEG activities by delta waves during various depth of anesthesia. We show that slow and fast delta waves (0-2 Hz and 2-4 Hz respectively) and high frequency components of the EEG (8-20 Hz) are correlated with each other and there is a kind of phase locking between them that varies with depth of anesthesia. Our analyses show that maximum amplitudes of high frequency components of the EEG signal are appeared in different phases of slow and fast delta waves when the concentration of Desflurane and Propofol anesthetic agents varies in a patient. There are some slight differences in using slow and fast components of delta... 

Local mean-field models (MFMs) describe regional brain activities by some connected differential equations. In an overall view, constituting variables of these differential equations can be divided to very fast, fast and slow variables. In this article we propose a method that can be used to determine role of a slow variable in behavior of MFMs. Very fast variables can be adiabatically removed from the equations. Isoclines of fast and slow variables and their corresponding vector field can provide valuable information about model behavior and role of the slow variable in it. The vector field of our interested MFM that is an enhanced MFM designed specially for general anesthesia, is a 3D... 

Objectives: Investigation of the amplitude modulation of alpha-band EEG oscillations (i.e., grouping of alpha-band activities) by delta-band EEG activities in various depths of anesthesia (DOA). Methods: This modulation, which is a sort of phase dependent amplitude modulation, is studied in 10 children in various depths of Desflurane anesthesia. Two parameters are defined to quantify the modulation: strength of modulation (SOM) and phase of modulation (POM). SOM indicates to what extent delta and alpha activities are related to each other, and POM is the delta phase in which the alpha amplitude is maximal. These parameters are analyzed in different DOA for various formations of delta... 

In this paper, the performance of a novel HDH solar desalination system equipped with a combination of heat pipe (HP), evacuated tube collector (ETC) and air bubble column humidifier is experimentally investigated. This novel HDH system uses advantages of ETC-HP as a highly efficient thermal absorption and conductor device, and at the same time employs the advantages of an air bubble column humidifier, i.e. high interface area and effective mixing in order to heat the water and humidify the air, respectively. The effects of various parameters including incoming air flow rate into the humidifier, initial depth of water in the humidifier, and adding fluids such as oil and water in the space... 

In order to design Button Beam Position Monitors (BPMs) for synchrotron facilities, one algorithm by C# have been developed which can calculate all required parameters to analyze optimal design based on vacuum chamber and button dimensions. Beam position monitors are required to get beam stabilities on submicron levels. For this purpose, different parameters such as capacitance, sensitivity versus bandwidth, intrinsic resolution, induced charge and voltage on buttons are calculated. Less intrinsic resolution and high sensitivity and capacitance are desired. To calculate induced charge and voltage on each button, Poisson's equation has been solved by Green method. For sensitivities... 

Image fusion means to integrate information from one image to another image. Medical images according to the nature of the images are divided into structural (such as CT and MRI) and functional (such as SPECT, PET). This article fused MRI and PET images and the purpose is adding structural information from MRI to functional information of PET images. The images decomposed with Nonsubsampled Contourlet Transform and then two images were fused with applying fusion rules. The coefficients of the low frequency band are combined by a maximal energy rule and coefficients of the high frequency bands are combined by a maximal variance rule. Finally, visual and quantitative criteria were used to... 

Echocardiography as a common place device has been widely used for diagnosis. Segmentation of left ventricle just using the standard level set formulation based on the magnitude of intensity cannot restrict the contour evolution completely due to the lack of sharp edges. This paper aims to introduce a robust method for segmentation of three-dimensional echocardiography images based on two boundary maps namely distance map and probabilistic map as stopping criteria of contour evolution which are extracted using two different approaches. The distance map is extracted by applying Free Form Deformation (FFD) method on manually determined landmarks of initial spherical volume to register the... 

In this paper, an efficient approach for ultrasonic object segmentation with special application for left ventricle segmentation in echocardiography images is proposed. At first, an efficient hybrid trend for ultrasonic image edge detection is suggested. Then, a modified level set approach is introduced based on the extracted edges and the computed probabilistic map as the stopping criteria for the contour evolution. Both synthetic and clinical images are utilized as validation measures with respect to the prior techniques which indicate outperform results quantitatively and qualitatively. Left ventricle segmentation using proposed method illustrates expert-approved performance, providing a... 

The substitution of conventional mathematical models with fast and accurate modeling tools can result in the further development of desalination technologies and tackling the need for freshwater. Due to the great capability of data-driven methods in analyzing complex systems, several attempts have been made to study various desalination systems using data-driven approaches. In this state-of-the-art review, the application of various artificial intelligence and design of experiment data-driven methods for analyzing different desalination technologies have been thoroughly investigated. According to the applications of data-driven methods in the field of desalination, the reviewed... 

Numerical approximation based on different forms of the governing partial differential equation can lead to significantly different results for two-phase flow in porous media. Selecting the proper primary variables is a critical step in efficiently modeling the highly nonlinear problem of multiphase subsurface flow. A comparison of various forms of numerical approximations for two-phase flow equations is performed in this work. Three forms of equations including the pressure-based, mixed pressure-saturation and modified pressure-saturation are examined. Each of these three highly nonlinear formulations is approximated using finite difference method and is linearized using both Picard and... 

Automatic extraction of the varying regions of magnetic resonance images is required as a prior step in a diagnostic intelligent system. The sparsest representation and high-dimensional feature are provided based on learned dictionary. The classification is done by employing the technique that computes the reconstruction error locally and non-locally of each pixel. The acquired results from the real and simulated images are superior to the best MRI segmentation method with regard to the stability advantages. In addition, it is segmented exactly through a formula taken from the distance and sparse factors. Also, it is done automatically taking sparse factor in unsupervised clustering methods...