Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Isar Nejadgholi; Mohammad Hasan Moradi; Fateme Abdol Ali
Volume 4, Issue 4 , June 2010, , Pages 279-292
Abstract
Many methods for automatic heartbeat classification have been applied and reported in literature, but relatively little number of them concerned with patient independent classification because of the less significant results compared to patient dependent ones. In this work, Reconstructed Phase Space ...
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Many methods for automatic heartbeat classification have been applied and reported in literature, but relatively little number of them concerned with patient independent classification because of the less significant results compared to patient dependent ones. In this work, Reconstructed Phase Space (RPS) theory is used to classify five heartbeat types (Normal, PVC, LBBB, RBBB and PB). In the first and second method, RPS is modeled by the Gaussian mixture model (GMM) and bins, respectively and then classified by classic Bayesian classifier. In the third method, RPS is directly used to train predictor time-delayed neural networks (TDNN) and classified based on minimum prediction error. All three methods highly outperform the results reported before for patient independent heartbeat classification. The best result is achieved using GMM-Bayes method with 92.5% accuracy for patient independent classification.
Fluid-Structure Interaction in Biological Media / FSI
Bahman Vahidi; Nasser Fatouraee
Volume 2, Issue 4 , June 2008, , Pages 285-296
Abstract
Arterial embolism is one of the major killers of the people who have heart diseases. In cerebral arteries, the danger of embolism is that the ruptured particles are carried into the brain, provoking neurological symptoms or a stroke. In this research, for the first time, we have presented a numerical ...
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Arterial embolism is one of the major killers of the people who have heart diseases. In cerebral arteries, the danger of embolism is that the ruptured particles are carried into the brain, provoking neurological symptoms or a stroke. In this research, for the first time, we have presented a numerical model to study the complete blockage of the human common carotid artery resulted from the physical motion of a blood clot bulk with spherical geometry in it. In the numerical model, a transient flow was assumed in an axisymmetric finite length tube. The incompressible Navier-Stokes equations were used as the governing equations for the fluid and a linear elastic model was utilized for the blood clot bulk. In order to model the contact conditions between the blood clot and arterial wall, an axisymmetric rigid contact model was used. The arbitrary Lagrangian-Eulerian formulation (ALE) was applied to analyze the solid large displacements inside fluid flow. The results indicated that during contact between stenosis and the clot, separation and reattachment regions were occurred on the stenosis extensively which are susceptible to thrombosis onset and growth. By abruption of the clot from the arterial wall during its passage through the stenosis, an extensive recirculation zone occurred downstream of the stenosis and beneath the moving clot bulk. Analysis of the clot motion and deformation have showed that when the clot passed the stenosis completely, the areas near the clot peak had a large tendency to expand which indicated the propensity of these areas to disperse.
Biomedical Image Processing / Medical Image Processing
Hadi Sabahi; Hamid Soltanian Zadeh; Lisa Scarpace; Tom Mikkelsen
Volume 5, Issue 4 , June 2011, , Pages 289-295
Abstract
In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatment response of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, ...
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In this paper, we propose a method to predict the outcome of Bevacizumab therapy on Glioblastoma Multiform (GBM) tumors. The method uses diffusion anisotropy indices (DAI) and spatial information to predict the treatment response of each tumor voxel. These DAIs are Fractional Anisotropy, Mean Diffusivity, Relative Anisotropy, and Volume Ratio, extracted from Diffusion Tensor Imaging (DTI) data before treatment. The spatial information is considered as the distance of each tumor voxel from the tumor center, extracted from pre-treatment post-contrast T1-weighted Magnetic Resonance Images (pc-T1-MRI). DAIs and spatial information of each tumor voxel are considered as feature vector. DTI and pc-T1-MRI are gathered before and after the treatment of seven GBM patients. First, DAIs of all brain voxels and the distance of each tumor voxel from the tumor center are calculated. Second, the method registers pretreatment DAI maps and post-treatment pc-T1-MRI to pre-treatment pc-T1-MRI. Next, the tumor is segmented using thresholding technique from pc-T1-MRI. Then, Gd-enhanced voxels of the pre- and posttreatment pc-T1-MRI are compared to label the feature vectors. Three classifiers were evaluated, including Support Vector Machine, K-Nearest Neighbor, and Artificial Neural Network. Classification results show a preference for K-Nearest Neighbor based on well-established performance measures.
Biomedical Image Processing / Medical Image Processing
Maryam Dorvashi; Neda Behzadfar
Volume 15, Issue 4 , March 2022, , Pages 289-298
Abstract
Early detection of fatigue helps to improve the quality and effectiveness of neurofeedback training. Diagnosis of fatigue using the EEG signal of participants during neurofeedback training in 10 training sessions is reviewed in this paper. Neurofeedback training has two different neurofeedback training ...
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Early detection of fatigue helps to improve the quality and effectiveness of neurofeedback training. Diagnosis of fatigue using the EEG signal of participants during neurofeedback training in 10 training sessions is reviewed in this paper. Neurofeedback training has two different neurofeedback training protocols called protocols one and two. The first protocol is a training feature, a combination of frequency and non-frequency features, but the second protocol only includes frequency features. In the first fatigue time protocol, the slope trend of the power changes of the second low alpha sub-band in the OZ channel is decreasing and the permutation entropy in the FZ channel is increasing. The slope of the score changes is also decreasing. In the second protocol, the slope trend of power changes is the second low alpha sub-band in the OZ channel and decreases the score, in other words, the lack of feature change in line with the goal of neurofeedback training is due to fatigue and the participant cannot score. The results are based on the power slope trend of the second lower alpha sub-band and permutation entropy, which indicates that fatigue occurs for one participant in the first protocol and for three participants in the second protocol.
Biomedical Image Processing / Medical Image Processing
Marjan Iranianpour Haghighi; Seyyed Vahab Shojaeddini
Volume 10, Issue 4 , January 2017, , Pages 291-302
Abstract
Detecting lesion borders is the first step for intelligent lesion identification in dermoscopy, therefore it may influence the accuracy and validity of the next steps of this process. Unfortunately, extracting borders is hampered by some challenges such as losses associated with irregular borders, poor ...
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Detecting lesion borders is the first step for intelligent lesion identification in dermoscopy, therefore it may influence the accuracy and validity of the next steps of this process. Unfortunately, extracting borders is hampered by some challenges such as losses associated with irregular borders, poor contrast, and artifacts encountered in some area. In this paper, the improved version of energy function optimization technique is introduced in order to separate the skin and lesions in the processing of dermoscopy images. This technique is based on the concept of radial directions in the contour development process, which reduces the sensitivity of estimating the boundaries of lesions to the above constraints. The performance of the proposed method is evaluated on a dataset of dermoscopy images which are captured from various lesions with different sizes and boundaries. The obtained results of the proposed method are compared with some other state-of-the-art lesion detection methods by using standard parameters. Increased True Detection Rate by 6.17% in parallel with decrease in Hammoud Distance by 2.3%, both compared to the best among alternative methods shows the effectiveness of the proposed scheme in detecting lesion borders of dermoscopy images.
Biological Computer Modeling / Biological Computer Simulation
Reza Vosoughi; Armin Allahverdy; Sajjad Shafiekhani; Amir Homayoun Jafari
Volume 11, Issue 4 , February 2018, , Pages 291-301
Abstract
In recent decades, due to the increased prevalence of diabetes and its chronic complications, glucose measurement, modeling of glucose-insulin system and glucose control have been especially important. Since the type I diabetes does not secrete insulin, cells do not absorb glucose, and thus the blood ...
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In recent decades, due to the increased prevalence of diabetes and its chronic complications, glucose measurement, modeling of glucose-insulin system and glucose control have been especially important. Since the type I diabetes does not secrete insulin, cells do not absorb glucose, and thus the blood glucose level increase. In order to control your blood sugar, insulin should besubcutaneously injected into the body under complex, controlled conditions. If the level of insulin increases beyond the natural physiological range, there is a risk of death. There are various treatments for diabetes, the main treatment of which is insulin therapy. Monitoring the patient's blood sugar level continuously during the day and night is a very good treatment strategy, since it controls the patient's blood sugar level in a safe area with the lowest amount of insulin injected at the required times. This mechanism avoid the hyperglycemia (blood glucose levels greater than 120 mg/dl) and hypoglycemia (blood sugar less than 65 mg / dl). To achieve this goal, a two delay model has been developed to model blood glucose levels continuously during time. Some of the parameters of this model are estimated using the genetic algorithm to achieve the best fitness between the dynamics of the model with the experimental data obtained in this study. As a result, the developed model of this study can dynamically obtain blood glucose continuously during time, consequently it can predicts the insulin dynamics required to be injected into the patient to control the amount of blood glucose in the normal range. Therefore this controlling system is capable of preventing hypoglycemia and hyperglycemia.
Neda Kaboodvand; Farzad Towhidkhah; Behzad Iravani; Shahriar Gharibzadeh
Volume 7, Issue 4 , June 2013, , Pages 297-310
Abstract
The central nervous system (CNS) uses a redundant set of joints and muscles to ensure both flexible and stable movements. How the CNS faces the complexity of control problem is not still clear. Modular control is one of the most attractive hypotheses in motor control. In this hypothesis, some motor primitives ...
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The central nervous system (CNS) uses a redundant set of joints and muscles to ensure both flexible and stable movements. How the CNS faces the complexity of control problem is not still clear. Modular control is one of the most attractive hypotheses in motor control. In this hypothesis, some motor primitives (e.g. muscle synergies) are considered as the building blocks that can be combined to present a vast repertoire of movements. EMG signals are required for extracting muscle synergies and NMF (nonnegative matrix factorization) is one of the most accepted methods for extracting synergies. Due to tonic component elimination of EMG signals involved in reaching movements in vertical planes, the standard NMF method is not applicable to extract muscle synergies. In this paper a modified NMF method, so-called semi-NMF, is applied to resolve the tonic component problem. On the other hand, to improve the accuracy of synergies' estimation and to find the global optimum for the optimization problem, we have proposed using HALS method. The proposed algorithm was applied to the experimental EMG recorded in arm reaching movement in the frontal plane. The results showed a good improvement both in accuracy and repeatability of extracted synergies. In addition, extracted muscle synergies were physiologically interpretable.
Maryam Saidi; Seyed Mohammad Firoozabadi
Volume 13, Issue 4 , December 2019, , Pages 303-314
Abstract
Transcranial Direct Current Stimulation (tDCS) is a non-invasive brain stimulation technique that is affordable and easy to operate compared to other neuromodulation techniques. Despite this method is promising in treating neurological diseases and enhancing cognitive functions, the precise mechanism ...
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Transcranial Direct Current Stimulation (tDCS) is a non-invasive brain stimulation technique that is affordable and easy to operate compared to other neuromodulation techniques. Despite this method is promising in treating neurological diseases and enhancing cognitive functions, the precise mechanism of the effect of this sub-threshold stimulation has not been understanded well. Understanding the mechanism is important in designing the proper protocol and system for the brain's electrical stimulation. The aim of this paper is to identify this mechanism with the neural modeling approach. As the results of some physiological studies have shown that under tDCS, sudden calcium signaling associated with calcium signaling of astrocyte cells in the brain are found, in the proposed model, this cell is considered as well as the main neurons and interneurons. The purpose of this model is to simulate the effect of tDCS on cortical activity related to the evoked response potential (ERP) and to compare with the actual results of previous experimental studies on rats. The results show that this model can simulate all the evidence of experimental studies, while the proposed purely neuronal model in previous studies could not simulate all the evidence.
Biomedical Image Processing / Medical Image Processing
Ali Kermani; Ahmad Ayatollahi; Sorour Mohajerani
Volume 8, Issue 4 , February 2015, , Pages 325-337
Abstract
IVUS imaging is a minimally invasive blood vessel cross-sectional imaging procedure in which accurate data is obtained from what is in there. Processing on these images or raw signals can provide wide range information for experts and practitioners, and can help them in making an accurate diagnosis and ...
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IVUS imaging is a minimally invasive blood vessel cross-sectional imaging procedure in which accurate data is obtained from what is in there. Processing on these images or raw signals can provide wide range information for experts and practitioners, and can help them in making an accurate diagnosis and appropriate treatment. Extraction of tissue boundaries in the blood vessels is one of the challenging parts as a first step in this direction. In this paper a new method was proposed based on the minimax technique and connected components for extracting Adventitia tissue boundary in intravascular ultrasound images. For this purpose, initial boundary will be extracted using improved minimax technique. Then final boundary is extracted with high precision using connected components. The method was tested on a set of real data with regard to the Hausdorff distance and Jaccard index to evaluate its performance. Mean of Hausdorff distance and mean of Jaccard index were obtained 95% and 0.45 millimeter, consequently. These results show that the proposed method in this paper can extract Adventitia tissue boundaries more accurately than existing methods with regard to the distance Hausdorff distance and Jaccard index.
Bioelectrics
Hamid Heydari Nejad; Hadi Delavari
Volume 9, Issue 4 , February 2015, , Pages 327-339
Abstract
The patients with Type 1 diabetes need strict blood glucose level control because the body’s production and use of insulin are impaired and hence this increases the blood glucose level. In this paper, a fractional order sliding mode control and an adaptive fractional order sliding mode control ...
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The patients with Type 1 diabetes need strict blood glucose level control because the body’s production and use of insulin are impaired and hence this increases the blood glucose level. In this paper, a fractional order sliding mode control and an adaptive fractional order sliding mode control are proposed to regulate the blood glucose in the presence of the parameter variations and meal disturbance. The Bergman minimal model is used to design the proposed controllers. The proposed controllers are appropriate for making the insulin delivery pumps in closed loop control of diabetes. The proposed controllers attenuate the effect of chattering. The fractional adaptive sliding mode control makes the controller immune to disturbance and uncertainties and the fractional calculus provides robustness performance. Finally the results are compared with some other methods such as backstepping sliding mode control and fractional order sliding mode control methods. Simulation results show that the proposed controllers are able to reject both uncertainties and disturbance with a chattering free control law.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Javad Delavar Matanaq; Hamed Danandeh Hesar; Mohammad Hadi Ahmadi fam
Volume 17, Issue 1 , May 2023, , Pages 11-20
Abstract
In recent years, model-based ECG processing algorithms have been successfully developed in various fileds of ECG processing. The calculation of ECG dynamic model (EDM) is a crucial step for these methods. The EDM parameters can be calculated using optimization algorithms. One of the popular optimization ...
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In recent years, model-based ECG processing algorithms have been successfully developed in various fileds of ECG processing. The calculation of ECG dynamic model (EDM) is a crucial step for these methods. The EDM parameters can be calculated using optimization algorithms. One of the popular optimization methods in this field is an offline nonlinear method in which users have to manually select points on ECG signal in order to calculate EDM parameters. The objective function used in this algorithm is a complex function which is hard to optimize. In this paper an automatic optimization algorithm is proposed which uses meta-heuristic optimization algorithms to calculate EDM parameters. In this algorithm, we don’t need to select points manually. In addition, the objective function in this algorithm is broken in to several simple objective functions which makes the optimization more accurate. Meta-heuristic optimization algorithms may perform successfully on some optimization problems while failing on others. As a result, a specific algorithm cannot be considered the best optimizer for all optimization problems. For this reason, in this paper, the performances of nine popular meta-heuristic algorithms such as particle swarm optimization, artificial bee colony, cucko search, etc are investigated. In this paper, 200 ECG segments from different records of the MIT-BIH Normal Sinus Rhythm Database (NSRDB) have been selected for evaluation. The duration of each segment was 30 seconds. The EDM parameters for each segment were calculated using the aforemetinoned optimization algorithms. For evaluation, the similarities between the original signals and the synthetic ECG signals were inspected for each optimization algorithm. These synthetic signals were created using the calculated EDM parameters. The similarity results showed that the water evaporation optimization (WEO), teaching learning-based optimization (TLBO), and cucko’s search (CS) algorithms achived better results compared with other methods.
Speech processing
Yaser Shekofteh; Farshad Almasganj
Volume 6, Issue 1 , June 2012, , Pages 17-33
Abstract
Recent researches show that nonlinear and chaotic behavior of the speech signal can be studied in the reconstructed phase space (RPS). Delay embedding theorem is a useful tool to study embedded speech trajectories in the RPS. Characteristics of the speech trajectories have rarely used in the practical ...
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Recent researches show that nonlinear and chaotic behavior of the speech signal can be studied in the reconstructed phase space (RPS). Delay embedding theorem is a useful tool to study embedded speech trajectories in the RPS. Characteristics of the speech trajectories have rarely used in the practical speech recognition systems. Therefore, in this paper, a new feature extraction (FE) method is proposed based on parameters of vector AR (VAR) analysis over the speech trajectories. In this method, using filter and reflection matrices obtained from applying VAR analysis on static and dynamic information of the speech trajectory in the RPS, a high-dimensional feature vector can be achieved. Then, different transformation methods are utilized to attain final feature vectors with appropriate dimension. Results of discrete and continuous phoneme recognition over FARSDAT speech corpus show that the efficiency of the proposed FE method is better than other time-domain-based FE methods such as LPC and LPREF.
Biomedical Image Processing / Medical Image Processing
Ladan Amini; Hamid Soltanian Zadeh; Caro Lucas; Masoume Giti
Volume -2, Issue 1 , July 2005, , Pages 17-34
Abstract
Based on a discrete dynamic contour model, a method for segmentation of brain structures like thalamus and red nucleus from magnetic resonance images (MRI) is developed. A new method for solving common problems in extracting the discontinuous boundary of a structure from a low contrast image is presented. ...
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Based on a discrete dynamic contour model, a method for segmentation of brain structures like thalamus and red nucleus from magnetic resonance images (MRI) is developed. A new method for solving common problems in extracting the discontinuous boundary of a structure from a low contrast image is presented. External and internal forces deform the dynamic contour model. Internal forces are obtained from local geometry of the contour, which consist of vertices and edges, connecting adjacent vertices. The image data and desired image features such as image energy are utilized to obtain external forces. The problem of low contrast image data and unclear edges in the image energy is overcome by the proposed algorithm that uses several methods like thresholding, unsupervised clustering methods such as fuzzy C-means (FCM), edge-finding filters like Prewitt, and morphological operations. We also present a method for generating an initial contour for the model from the image data automatically. Evaluation and validation of the methods are conducted by comparing radiologist and automatic segmentation results. The average of the similarity between segmentation results is 0.8 for the left and right thalami indicating excellent performance of the new method. Additional noise and intensity inhomogeneity changed the evaluation results slightly illustrating the robustness of the proposed method to the image noise and intensity inhomogeneity.
Biopolymers
Ruhollah Mahdinavaz Maghdam; Hamid Mirzadeh; Morteza Daliri; Alireza Zare
Volume 1, Issue 1 , June 2007, , Pages 19-27
Abstract
Recently there are studies in developing new methods to increase bacterial adhesion onto polymeric surfaces that are used in biological application such as cell-based biosensors. In this study the surface of polyethylene terephthalate (PET) films were irradiated using CO2 and KrF excimer pulsed ...
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Recently there are studies in developing new methods to increase bacterial adhesion onto polymeric surfaces that are used in biological application such as cell-based biosensors. In this study the surface of polyethylene terephthalate (PET) films were irradiated using CO2 and KrF excimer pulsed lasers and adhesion behavior of Escherichia coli k-12 (E. coli K-12) bacteria onto the irradiated surfaces was studied in vitro. The changes in the surface properties due to laser irradiation were characterized by scanning electron microscopy (SEM) and contact angle measurement. The results showed that laser treatment changes surface morphology and surface hydrophilicity. The number of bacteria that were adhered onto the surfaces was quantitatively investigated by fluorescent staining, microscopic observations and counting through Image Proplus software. The results showed that the number of adhered E. coli K-12 bacteria onto the irradiated surfaces by both CO2 and KrF lasers in comparison with unmodified surfaces was increased.
Rehabilitation Engineering
Ziba Gandomkar; Fariba Bahrami
Volume 7, Issue 1 , June 2013, , Pages 21-37
Abstract
Changes in gait pattern are early symptoms in many disorders such as balance and control problems resulted in fall among elderlies. This paper aims at proposing a new set of features extracted from Gait Frieze Pattern (GFP) in order to classify seniors to fallers and non-fallers. For indicating the effectiveness ...
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Changes in gait pattern are early symptoms in many disorders such as balance and control problems resulted in fall among elderlies. This paper aims at proposing a new set of features extracted from Gait Frieze Pattern (GFP) in order to classify seniors to fallers and non-fallers. For indicating the effectiveness of the presented method, the algorithm is used for recognition of different type of abnormal gaits. The introduced method consists of three main steps: extracting the subject from background, generating GFP and aligning them, and building the proposed image from GFP by thresholding followed by morphological operations. For evaluation of the proposed features, video sequences are collected from 8 elderly fallers, 8 non-fallers, and 8 youth while performing standard Timed Up and Go (TUG) test. In addition to TUG test youths are asked to walk fast and pretend to walk with 6 different types of abnormalities (limping, waddling, anterior- posterior sway, lateral sway, dragging, steppage gait). For finding correct classification rate, each time one data is considered as test and others as train and label of train data with the most similarity with test one on the score of normalized cross correlation is assigned to test data. Comparing to conventional TUG test, correct classification data is improved around 20% for faller detection. In addition, correct classification rate for detecting of different abnormalities in gait is approximately 90%.
Biomechanics of Bone / Bone Biomechanics
Mohammad Nikkhoo; Mohammad Haghpanahi; J. L. Wang; Mohammad Parnianpour
Volume 5, Issue 1 , June 2011, , Pages 21-32
Abstract
Prediction of the relationship between different types of mechanical loading and the failure of the intervertebral disc is so important to identify the risk factors which are difficult to study in vivo and in vitro. On the basis of finite element methods some of these issues may be overcome ...
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Prediction of the relationship between different types of mechanical loading and the failure of the intervertebral disc is so important to identify the risk factors which are difficult to study in vivo and in vitro. On the basis of finite element methods 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 lumbar motion segment 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 short-term, long-term creep tests and cyclic loading were investigated. The results of the poroelastic model are in agreement with experimental ones reported in the literature. Hence, this model can be used to study how different dynamic loading regimes are important as risk factors for initiation of intervertebral disc degeneration.
Cell Biomechanics / Cell Mechanics / Mechanobiology
Morteza Khalilian; Mehdi Navidbakhsh; Mojtaba Rezazade; Mahmoud Chizari; Poupak Eftekhari Yazdi
Volume 2, Issue 1 , June 2008, , Pages 21-28
Abstract
Recently, considerable biomedical attention has centered on the mechanical properties of living tissues at the single cell level. Stiffness is an important parameter in determining the physical properties of living tissues. Indeed, stiffness changes of the ovum as a single cell pose a unique challenge ...
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Recently, considerable biomedical attention has centered on the mechanical properties of living tissues at the single cell level. Stiffness is an important parameter in determining the physical properties of living tissues. Indeed, stiffness changes of the ovum as a single cell pose a unique challenge in determining the sequence of fertilization. The ovum's extracellular layer has been reported to be altered following fertilization in a process described as Zona reaction. In the present study, the Young's modulus of Zona Pellucida of the mouse ovum was evaluated using micropipette aspiration technique. By incorporating exact engineering principles into the cell mechanics and extract appropriate formula, the Young's modulus of metaphase II (MII) and pronuclear (PN) was measured. The experimental results clearly demonstrated that the mouse Zona Pellucida hardened following fertilization. This study involves the contents of Reproductive Biology and Mechanics, and opens up a new trail of thought for evaluating the quality of mammalian oocytes and embryos.
Cell Biomechanics / Cell Mechanics / Mechanobiology
Seyed Abed Hosseini; Mohammad Ali Khalilzadeh; Seyed Mehran Homam
Volume 4, Issue 1 , June 2010, , Pages 23-31
Abstract
Various stressful stimuli have different effects on health, decision making, creativity, learning and memory. Understanding human mental states such as stress can prevent its long-term side effects on the body and mind. This study deals with the responses of the neural and hormonal systems to stress ...
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Various stressful stimuli have different effects on health, decision making, creativity, learning and memory. Understanding human mental states such as stress can prevent its long-term side effects on the body and mind. This study deals with the responses of the neural and hormonal systems to stress using the brain cognitive map in this state and simulates the behavior of the CA1 cell calcium channels with electrophysiological equations in the NEURON software. During stress, the glucocorticoids hormones secreted by the adrenal gland cortex reach the hippocampus through blood flow and by activating glucocorticoids receptors, influence the calcium channels dynamics, especially the L-type and increase calcium entry into CA1 cells. This behavior, testify to the reduction of the calcium removal rate in the cells which leads to exponential decrease in cells firing rate and number of spikes and an increase in the sAHP current range. L-type calcium currents in hippocampus region are effective mechanisms during stress. Comparing the research results in two situations, the cell under control and the cell under stress, shows that the model is consistent with some basic observations of stress.
Biomechanics / Biomechanical Engineering
Hadi Taghizadeh
Volume 14, Issue 1 , May 2020, , Pages 23-30
Abstract
Determining mechanical properties of very soft tissues have been considered as a popular and challenging topic in biomechanics only in the last decades. In addition, these tissues do not have any weight-bearing functions, however, their mechanical characterization is important for designing new safety ...
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Determining mechanical properties of very soft tissues have been considered as a popular and challenging topic in biomechanics only in the last decades. In addition, these tissues do not have any weight-bearing functions, however, their mechanical characterization is important for designing new safety equipment, diagnosis and treatment of the diseases and tumors. Liver is one of the vital body organs that is highly porous and tearable and is highly susceptible to mechanical damage during accidents and minimally invasive surgeries. In this study, a set of uniaxial tension tests was performed on a bovine liver tissue. Linear elastic model in combination with the Bridgman correction method was utilized to determine the mechanical properties, i.e., Young’s modulus. An image processing software was also developed utilizing MS Visual Fortran language in order to obtain and track required geometric dimensions, i.e., radii of curvature, minimal sample radius in the necking zone, and probable detaching during the test session. Our experiments showed a tensile elastic modulus of 15.51±1.62 kPa for the samples (p < 0.05). Different amounts for elastic modulus of the liver have been reported in the literature. Hence, we conducted tension tests on samples with progressively increasing diameters. The changes in the sample diameter was in the range of 2.5 to 20 mm. In this way, the effect of sample diameter on elastic modulus was inquired. Our results indicate an inverse relation between elastic modulus and diameter in the tested zone. Such phenomena can be attributed to small sample size which is similar to the size of liver lobules (a few millimeters). Hence, samples with diameters in the range of lobule size cannot constitute a suitable representative element for the liver tissue. To obtain valid results the sample diameters should be more than three times that of the lobule.
Medical Physics / Biomedical Physics / Medical Biophysics / Biophysics
Hossein Mirzaei; ,Ghazale Geraily,; Fatemeh Seyyedrezaei; Ali Kazemian
Volume 16, Issue 1 , May 2022, , Pages 23-32
Abstract
In radiotherapy treatments, there are some situations where the shape of the target volume is complicated, so multiple radiation fields are used to cover the whole tumoral tissue. Therefore, adjusting adjacent fields and minimizing radiation dose to healthy tissues is an important goal in radiotherapy. ...
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In radiotherapy treatments, there are some situations where the shape of the target volume is complicated, so multiple radiation fields are used to cover the whole tumoral tissue. Therefore, adjusting adjacent fields and minimizing radiation dose to healthy tissues is an important goal in radiotherapy. The aim of this study is to introduce a general mathematical solution for matching adjacent radiation fields and also to evaluate this solution in therapeutic techniques such as Craniospinal irradiation and breast with a supraclavicular full-field and half-field irradiation. This method considers a right-handed system with its center located in the isocenter and two hypothetical fields named field number one and field number two. Then, the angles of collimators, couch, gantry, and the jaw aperture for both hypothetical fields are calculated to match between their side plates using the presented methods. Comparison of the measurements with the treatment planning system shows that the Craniospinal radiotherapy technique has an error only in measuring the collimator angle; this error is 0.198%. The maximum errors were obtained in two supraclavicular techniques with full- and half-field in calculating the size of the jaw aperture of the supraclavicular field, which were 21.05% and 18.6%, respectively. In conclusion, according to the results, this method can improve the field alignment in all cases where adjacent treatment fields are used, with the acceptable error rate and without changing the field size, to achieve the same dose distribution.
Biomedical Image Processing / Medical Image Processing
Hamed Rakhshan; Hamid Behnam
Volume 3, Issue 1 , June 2009, , Pages 25-31
Abstract
Vibroacoustography is a relatively new elasticity imaging method that uses dynamic (oscillatory) radiation force of ultrasound to vibrate the tissue at low frequency (Kilo Hertz). The resulting acoustic emission is recorded with sensitive hydrophone to produce images that are related to the mechanical ...
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Vibroacoustography is a relatively new elasticity imaging method that uses dynamic (oscillatory) radiation force of ultrasound to vibrate the tissue at low frequency (Kilo Hertz). The resulting acoustic emission is recorded with sensitive hydrophone to produce images that are related to the mechanical properties of the tissue. This force is produced by two continuous overlapping ultrasound beams that have a slightly different frequency. Vibroacoustography has been applied to image breast and arteries microcalcification. The lateral resolution of this imaging method is about 0.7mm and its axial resolution is about 12 mm. In this paper two major methods of producing dynamic radiation force, Confocal and X-focal (consists of two concave transducers whose axes cross at their foci at an angle q), are analyzed. A new method for improving axial resolution using short duration pulses is introduced. Simulation results show that we have about 50% improvement in axial resolution using short duration pulses.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Vahid Abootalebi; Mohammad Hasan Moradi; Mohammad Ali Khalilzadeh
Volume -1, Issue 1 , June 2004, , Pages 25-45
Abstract
P300 is the most predominant cognitive component of the brain signals. In this study, the single trial event related potentials recorded from the scalp, were decomposed to their time-frequency components using discrete wavelet transform. These quantities were later analyzed as the features related to ...
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P300 is the most predominant cognitive component of the brain signals. In this study, the single trial event related potentials recorded from the scalp, were decomposed to their time-frequency components using discrete wavelet transform. These quantities were later analyzed as the features related to the cognitive activities of brain. Study on these features showed that cognitive processes of the brain of ten reflected in the feature of δ and θ bands. The aim of this study, as a primary step for "lie detection using brain signals (EEG - Polygraphy)", was to design a system for discriminating between single trials involved P300 and those without it. In the first approach, an optimal discriminant function based on 9 features was designed using "Stepwise Linear Discriminant Analysis". Detection accuracy was 75% in training data and 71% in test data. More study on this method showed that almost similar accuracy could be obtained from the features of Pz channel alone. In the second approach, the modular learning strategy - based on principal component analysis and neural networks - was used. After training the systems, the maximum classification accuracy was 76% in train data and 72% in test data.
Orthotics & Prosthesis
Marjan Bahraminasab
Volume 10, Issue 1 , May 2016, , Pages 25-40
Abstract
Knee implants still lacks sufficient design solutions to ensure improved long term performance without aseptic loosening and the subsequent revision surgery.The present paper, used full factorial design of experiment (DOE) method along with finite element analysis (FEA) to assess the influence of internal ...
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Knee implants still lacks sufficient design solutions to ensure improved long term performance without aseptic loosening and the subsequent revision surgery.The present paper, used full factorial design of experiment (DOE) method along with finite element analysis (FEA) to assess the influence of internal contours of femoral component on mechanical stability of the prosthesis, and the biomechanical stresses experienced by the femoral component, bone cement and the adjoining bone with preservation of the external contours.The WASPAS approach, as a multi criteria decision analysis (MCDA) technique, was then used to rank the alternative designs. The results of analysis of variance showed that the internal shape of femoral component contours influenced the performance measureswhere the angle between the distal and anterior cuts, the angle between the distal and posterior cuts, and the cement thickness were highlysignificant. The predictive mathematical models of each performance measureswre also estimated through statistical analysis. The ranking order and the following sensitivity analysis revealed that the top designs mostly had higher cement thickness and the original design was not the top choice for femoral component which by improving the current designbetter long term performance can be achieved.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Raheleh Davoodi; Mohammad Hasan Moradi
Volume 12, Issue 1 , June 2018, , Pages 25-39
Abstract
Depression is one of the most common mental disorders in the current century where early diagnosis can result in better treatment. One of the depression diagnostic methods is the analysis of the brain electrical signals. In this paper, we are seeking for a method to distinguish among the levels of the ...
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Depression is one of the most common mental disorders in the current century where early diagnosis can result in better treatment. One of the depression diagnostic methods is the analysis of the brain electrical signals. In this paper, we are seeking for a method to distinguish among the levels of the depression. The proposed model is a deep rule-based system based on the stacked principle and focuses on the interpretability of the rules alongside high accuracy. Fuzzy systems have the proper capability in the classification of medical data with various levels of uncertainty. Moreover, in the recent years, deep learning has been taken considerable attention in the field of Artificial Intelligence. In this paper, we aim to benefit from capabilities of both fields. The proposed architecture employs a robust fuzzy clustering approach that can determine an appropriate number of clusters in each layer, unsupervised and a hierarchical stacked structure to transfer the interpretable trained rules from the previous layers with the same linguistic labels to the next layer. The interpretability is due to the presence of the input space into the consequent ones. The presence of the output of the previous layer’s rules at the input space of the next parts equals to a fuzzy system with non-linear consequent or the certainty factor in a fuzzy system with linear consequent. EEG data were preprocessed and time, frequency and nonlinear features such as recurrent plot were extracted and selected and after that were employed in the proposed system. The proposed system was compared with common classifiers like Neural Net, Support Vector Machine, Naive Bayes, Decision Tree and Linear Discriminant Analysis. Accuracy results for the test data in 30 folds (49.01% in comparison to 41.42%, 40.47%, 40.01%, 38.35% and 40.28% respectively) demonstrate the considerable performance of the proposed system.
Biomedical Image Processing / Medical Image Processing
Seyed Hani Hojjati; Ataollah Ebrahimzadeh; Ali Khazaee; Abbas Babajani-Fermi
Volume 11, Issue 1 , May 2017, , Pages 29-40
Abstract
Predicting AD based on Brain network analysis has been the subject of much investigation. Here, we aim to identify the changes in brain in patients that conversion from (Mild Cognitive Impariment) MCI to AD (MCI-C) and non conversion from MCI to AD (MCI-NC), to provide an algorithm for classification ...
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Predicting AD based on Brain network analysis has been the subject of much investigation. Here, we aim to identify the changes in brain in patients that conversion from (Mild Cognitive Impariment) MCI to AD (MCI-C) and non conversion from MCI to AD (MCI-NC), to provide an algorithm for classification of these patients by using a graph theoretical approach. In this algorithm we proposed Discriminant Correlation Analysis (DCA), feature level fusion for multimodal biometric recognition method were applied to the original feature sets. An accuracy of 86/167% was achieved for predicting AD using the DCA and the support vector machine classifier. We also performed a hub node analysis and found the number of hubs in progressive AD patients. Indeed, this is the first neuroimaging study that integrates rs-fMRI with sMRI for detecting conversion from MCI to AD. The proposed classification method highlights the potential of using both resting state fMRI and MRI data for identification of the early stage of AD.
