Sharif Digital Repository

In the recent years a great attention of research deals with different physical and biological aspects of the BBB structure, a robust shield that separates the blood and brain, a recent research held by the authors of this paper has focused on figuring out computing the diffusion coefficient of endothelial cell membrane. In this study, the major efforts have been concentrated on calculating a standardized measure for the amount of permeability and diffusion of this barrier. As a result, this work is dedicated to molecular dynamics (MD) simulation of calculating the interaction force between nano-particle and BBB membrane. data is recorded by using steered molecular dynamics simulation and... 

Epilepsy is the most common neurological disorder that is known with uncontrolled seizure. Around 30% of patients with epilepsy resist to all forms of medical treatments and therefore, the removal of epileptic brain tissue is the only solution to get these patients free from chronical seizures. The precise detection of an epileptic zone is key to its treatment. In this paper, we propose a method of epilepsy detection using brain magnetic field. The possibility of superparamagnetic nanoparticle (SPMN) as sensors for the detection of the epileptic area inside the brain is investigated. The aggregation of nanoparticles in the weak magnetic field of epileptic brain is modeled using potential... 

System health management based on condition monitoring is of great value and significance for improving system performance and reliability. As more data from condition monitoring is obtained, the efficiency of system health monitoring improves. However, increasing the amount of data by installing new sensors on system components is restricted by a variety of factors such as budget, weight and space allowance. Therefore, sensor selection in the most optimal manner in order to achieve maximum valuable information from system is an important issue. In this study, a novel concept is introduced that can be used in sensor combination optimization problems. This concept employs failure modes and... 

Objective: Joint analysis of multi-subject brain imaging datasets has wide applications in biomedical engineering. In these datasets, some sources belong to all subjects (joint), a subset of subjects (partially-joint), or a single subject (individual). In this paper, this source model is referred to as joint/partially-joint/individual multiple datasets unidimensional (JpJI-MDU), and accordingly, a source extraction method is developed. Method: We present a deflation-based algorithm utilizing higher order cumulants to analyze the JpJI-MDU source model. The algorithm maximizes a cost function which leads to an eigenvalue problem solved with thin-SVD (singular value decomposition)... 

Point Set Registration (PSR) of anatomic parts is used in different fields such as Patient Specific Implant (PSI) design in Computer Assisted Surgery (CAS) procedure. We designed a modified rigid PSR method based on student's-t mixture model. The proposed method is compared with Coherent Point Drift (CPD) registration method. Higher convergence speed and the lower error value are the advantages of the suggested algorithm in compare with CPD. In our method, the number of iterations decreases by about 69%, and the final error improvement was about 7% in comparison with CPD. The robustness of the proposed algorithm makes it beneficial to be used in the procedure of designing PSI in both... 

Effective classification of motor imagery electroencephalograph (EEG) data is an important challenge. Spatial filtering such as Common Spatial Pattern (CSP) and its variants are commonly used for this task. However, CSP effectiveness depends on the subject-specific frequency band. Even by optimally selecting a subject-specific frequency band, this method still fails for some subjects. On the other hand, some studies suggest that temporal features may discriminate classes more efficiently. This work proposes a hybrid method based on elastic net and Least Absolute Shrinkage and Selector Operator (LASSO) to optimally select between spatial and temporal features. This algorithm uses joint... 

Computational models of the central nervous system after a stroke helps to reveal the physiological mechanisms that may have strong impacts on the neuro-motor rehabilitation approaches. This paper studies the stroke subject's motor control mechanism in reaching movements by extending the previous study by incorporating the kinematics of motion as well as neural disconnection between the muscles and the CNS to further develop a planar patient specific neuro-musculoskeletal model of arm. The developed model was calibrated to eight post-stroke individuals by altering the Muscle Significance Factors (MFS) using numerical optimization to match the simulated motions with those measured... 

Arterial diseases, namely atherosclerosis, are believed to be a product of abnormal changes in both hemodynamic and non-hemodynamic factors. In order to explornmk,e the role of different factors in initiation and progression of this disease, a CFD technique was applied to study Interaction between the structure of the artery and blood flow for different suggested models that were used to describe mechanics of vessel wall. This study presents a three-dimensional, steady state simulation of blood flow through the single and double layered carotid artery bifurcation using fluid structure interaction (FSI) method. The wall shear stress and mechanical stress/strain are computed and analyzed under... 

This paper presents a mathematical model describing how the physicians actually make a diagnostic decision. Next to a description of diagnostic decision making as done by the physicians, this paper shows that how can we design and develop a new approach to medical diagnosis based on the human principles of decision making. The model was challenged to diagnose a series of actual patients. Real clinical data was entered into the model and the system produced a ranked list of possible diagnoses for each case. The results indicated good performance when compared with internist's diagnosis. The proposed method is effective and can be applied to describe medical reasoning as done by the... 

The front part of a cell is divided to two regions called lamellum and lamellipodium (lamellipodial). This part plays an essential role for cell migration. Indeed, there are many protein filaments called actin in lamellum and lamellipodium, which induce the cell motion with polymerization in the leading edge of the cell. The actin filaments adhere to the extracellular matrix (ECM) by means of focal adhesions and they have contact by myosin motor proteins. The myosin motor proteins cause actin retrograde and anterograde flow exerted contractile stress on them. The focal adhesions exert frictional stress on the actin filaments. In this work, we developed a two-dimensional continuum model of... 

Understanding the relationship between different kinds of mechanical loading and the failure of the intervertebral disc is difficult to study in vivo and in vitro. Through finite element studies some of these issues may be overcome enabling more detailed assessment of the biomechanical behavior of the intervertebral disc. The objective of this paper is to develop a nonlinear axisymmetric poroelastic finite element model of intervertebral disc and show its capability for studying the time-dependent response of disc. After comparison of the response of different models in quasi-static analysis, the poroelastic model of intervertebral disc is presented and the results of a short-term and... 

The advent of microfluidics as suitable environments for culturing cells is associated with some challenges as shear stresses applied on the cells. Moreover, among all factors needed for cell viability, feeding hepatocytes with adequate oxygen is of great importance due to their high demand for oxygen compared the other cell types. In this paper three kinds of geometries has been studied in order that shear stresses would be in allowed range and provision of hepatocytes with sufficient oxygen concentrations has been ensured as well. In addition to supplying hepatocytes with oxygen, the range of its concentration has been adjusted in physiologic value so that it would be practical for further... 

Studies show that High frequency oscillations (HFOs) can be used as a reliable biomarker of epileptogenic zone, thus many algorithms have been proposed to detect HFOs. Among the wide variety of HFOs, fast ripples (FRs) are important transient oscillations occurring in the frequency band ranging from 250 Hz to 600 Hz. The automatic detection of FRs can be degenerated by the presence of some 'pulse-like' events (commonly, the component of interictal epileptic spikes) associated with an increase of the signal energy in the high frequency bands, exactly as in the case of real FRs. The goal of this study is to propose a new method for automatic detection of fast ripples by using Hidden Markov... 

Automatic cell segmentation has various applications in different parts of science. The development of automated methods for cell segmentation, remains challenging in situations where there are touching cells. In this paper we propose a new method for separating touching cells. As the first step, we use a combination of graph segmentation algorithm and thresholding for segmenting foreground objects and producing a binary image. Next, boundary points of separation zone are selected by using a corner detection algorithm. Finally, the marker controller watershed transform is applied to separate touching cells at selected points. © 2008 IEEE  

Electrocardiogram is one of the most prominent cardiac signals being capable to be utilized for medical uses such as arrhythmia detection. Over the years, the feasibility of using this signal for human identification issue has been investigated, and some methods have been proposed. In this Paper a novel approach is proposed for electrocardiogram (ECG) based human identification using Extended Kalman Filter (EKF). The innovation signal of EKF has been considered as feature which is used to classify different subjects. In this paper a general issue, human identification, is summarized to a classification problem in which the proposed features of each subject is calculated, and the... 

In this Paper a new approach is proposed for electrocardiogram (ECG) based human identification using wavelet distance measurement. The main advantage of this method is that it guarantees high accuracy even in abnormal cases. Furthermore, it possesses low sensitivity to noise. The algorithm was applied on 11 normal subjects and 10 abnormal subjects of MIT-BIH Database using single lead data, and a 100% human identification rate was on both normal and abnormal subjects. Adding artificial white noise to signals shows that the method is nearly accurate in SNR level above 5dB in normal subjects and 20dB in abnormal subjects  

The aim of this research is to design an implantable integrated microfluidic system in order to regularly measure the glucose level in the human body, nonenzymatically, using the microdialysis method. The main compartments of this system are a micropump, array of hollow microneedles and an electrochemical sensor. At the base of the microneedles, there are located semipermeable membranes, that when the pumped dialysis fluid passes over them, the glucose of the interstitial fluid diffuses into the dialysis fluid and then, in the sensor section, it is measured nonenzymatically using the amperometry method. Both the arrangement of the miconeedles and the amount of the dialysis fluid flow are... 

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... 

Detection of seizure periods in an epileptic patient is an important part of health care. However, due to the variety in types of seizures and location of them, real-time seizure detection is not straight forward. In this paper, we propose a method for seizure detection from EEG signals in datasets which have both generalized and focal seizures. The proposed method is useful in the situations that we have no prior knowledge about the location of the patient's seizure and the pattern of evolution of seizure location. In the proposed method, first, the artifacts are automatically reduced by Blind Source Separation (BSS) methods. Then, the channels are clustered into two clusters. After that,... 

This study presents a new method for epilepsy detection based on autoregressive (AR) estimation of EEG signals. In this method, optimum order for AR model is determined by Bayesian Information Criterion (BIC) and then AR parameters of EEG signals (from EEG data set of epilepsy center of the University of Bonn, Germany) and their sub-bands (created with the help of wavelet decomposition) are extracted based on it. These parameters are used as a feature to classify the EEG signals into Healthy, Interictal (seizure free) and Ictal (during a seizure) groups using multilayer perceptron (MLP) classifier. Correct classification scores at the range of 91% to 96% reveals the potential of our approach...