Biomechanics / Biomechanical Engineering
Mehran Ashrafi; Farzan Ghalichi; Behnam Mirzakouchaki
Volume 10, Issue 2 , August 2016, , Pages 113-121
Abstract
Nowadays, the use of dental implants in people with osteoporosis is increasing. The consequences of osteoporosis can be important to the success of osteosynthesis devices, prosthetics and dental implants. Using bisphosphonates, which with impressing bone remodeling and decreasing bone catabolic activity ...
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Nowadays, the use of dental implants in people with osteoporosis is increasing. The consequences of osteoporosis can be important to the success of osteosynthesis devices, prosthetics and dental implants. Using bisphosphonates, which with impressing bone remodeling and decreasing bone catabolic activity lead to increase bone formation can be used as a solution to increase bone density in patients with osteoporosis, which normally osteoporosis is considered as a risk to the acceptance of dental implants by alveolar bone. This study examines the effect of different concentrations of bisphosphonates on bone remodeling. By improving bone remodeling model and taking into account the drug concentration effect on bone resorption, drug effect will be considered. For this purpose, 5, 10 and 20 mg of alendronate per implant and control sample are simulated for a period of 360 days. By comparing the results with control sample, with increasing the drug dose, decrease in bone stress, increase in bone density and thus increase in young's modulus was observed.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Faezeh Daneshmand-Bahman; Ateke Goshvarpour
Volume 16, Issue 2 , September 2022, , Pages 115-131
Abstract
Anxiety disorders are one of the most common and debilitating mental disorders worldwide. On the other hand, since 2019, with the outbreak of Covid-19, anxiety has increased among people, especially the medical staff. Currently, anxiety is diagnosed (when the symptoms are severe enough) using a questionnaire ...
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Anxiety disorders are one of the most common and debilitating mental disorders worldwide. On the other hand, since 2019, with the outbreak of Covid-19, anxiety has increased among people, especially the medical staff. Currently, anxiety is diagnosed (when the symptoms are severe enough) using a questionnaire by a specialist. To resolve this shortcoming, researchers have recently paid attention to the use of brain signals. Consequently, the present study aimed to diagnose anxiety using brain signals. The novelty of this study is the use of the Chebyshev chaotic map for the first time in biological signal analysis. It used the DASPS database, which includes a 14-channel electroencephalogram (EEG) of 23 people (10 men and 13 women, with a mean age of 30 years). The self-assessment manikin scores were used to divide anxiety into two and four levels. First, the data were normalized. Then, the chaotic map was reconstructed and divided into 128 strips. The density of points in each of the strips was calculated. Two indicators were considered as features, (1) maximum density and (2) its corresponding sample. Finally, features were applied to Support Vector Machines (SVM) and k-Nearest Neighbors (K-NN) in 5 ways, (1) feature 1 of all channels, (2) feature1 mapping of all channels using principal component analysis (PCA), (3) feature 2 of all channels, (4) feature 2 mapping of all channels using PCA and (5) each feature - each channel separately. The results show a maximum accuracy of 93.75% for diagnosing two levels of anxiety and 96.15% for diagnosing four levels of anxiety. In addition, K-NN outperformed SVM. Accordingly, the proposed algorithm can be introduced as a suitable approach for diagnosing anxiety.
Biomedical Image Processing / Medical Image Processing
Hamid Abrishami Moghaddam; Alireza Sheikh Hasani; Abbas Mostafa; Masoume Giti; Parviz Abdolmaleki
Volume -1, Issue 2 , June 2005, , Pages 117-128
Abstract
This paper presents a CAD system for detection and diagnosis of microcalcification clusters in mammograms. The proposed algorithm is composed of three main stages. In the first stage, the image pixels are examined for corresponding to individual microcalcification objects. For this purpose, the wavelet ...
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This paper presents a CAD system for detection and diagnosis of microcalcification clusters in mammograms. The proposed algorithm is composed of three main stages. In the first stage, the image pixels are examined for corresponding to individual microcalcification objects. For this purpose, the wavelet transform of the image is computed. Then two wavelet coefficients as well as two statistical features are used with a neural network for a primary classification of the image pixels. In the second stage, some noisy pixels extracted by the first step are eliminated. Then 18 features defined for each microcalcification are used with a nonlinear classifier for accurate detection of microcalcifications. For training of this classifier we used 16 regions from a database containing 379 microcalcifications. Finally, in the third stage five features defined for each microcalcification cluster with a neural network are used to recognize malignant microcalcification clusters. For training of this network, 22 clusters including 8 malignant and 14 benign cases were used. The performance of the algorithm was evaluated using a separate image set composed of 22 clusters including 10 malignant and 12 benign cases. Using these tests images and the threshold value of 0.45, the sensitivity of the algorithm was 100% and its specificity was 91.6%.
Biological Computer Modeling / Biological Computer Simulation
Gelare Valizadeh; Fateme Fatemi; Mahmoud Shahabadi; Mohammad Ali Oghabian; Majid Pouladian
Volume 8, Issue 2 , June 2014, , Pages 125-133
Abstract
MTDDS is an innovative treatment modality to completely tumor remission with no negative side effect. In this method functionalize magnetic nanoparticles are designed as the drug carrier to get the specific target in the body. Anticancer agents are bounded to magnetite nanoparticles with biocompatible ...
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MTDDS is an innovative treatment modality to completely tumor remission with no negative side effect. In this method functionalize magnetic nanoparticles are designed as the drug carrier to get the specific target in the body. Anticancer agents are bounded to magnetite nanoparticles with biocompatible starch coating suspended in the fluid. Now if they are injected intra-arterially near the target volume, they would be trapped at the target region via a local applied magnetic field with the high gradient near the target site. In this paper we have evaluated some nanoparticle trajectories with different size in order to evaluate the effect of the size on the efficiency of the magnetic drug targeting system.
Artificial Organs
Erfan Nammakie; Hanieh Niroomand Oscuii; Farzan Ghalichi; Mojtaba Koochaki
Volume 9, Issue 2 , July 2015, , Pages 133-142
Abstract
Myocardial diseases are on the rise all over the world and due to lack of sufficient donors, heart transplants are not the perfect solutions to treat all patients with heart failure. Therefore, in recent years, blood pumps have received a worldwide admissibility and have become the unrivalled tools for ...
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Myocardial diseases are on the rise all over the world and due to lack of sufficient donors, heart transplants are not the perfect solutions to treat all patients with heart failure. Therefore, in recent years, blood pumps have received a worldwide admissibility and have become the unrivalled tools for replacing a failed heart. In addition to biological needs such as sufficient head and flow rate, an assist blood pump should be in an acceptable margin of safety in terms of blood injuries such as hemolysis and thrombosis. Reducing blood damages, minimizing dimensions, reducing exposure time and simulating blood flow of natural heart are amongst the greatest challenges in designing assist blood pumps. One of the most important factors in determining the amount of blood injuries inside the pump is the blades’ shape of different parts of the pump. Studies have been conducted about heart pumps show that it is feasible to increase the efficiency of the pump and reduce the stagnation points that lead tothrombus formation by changing the type of blades of the impeller. The purpose of this study is to compare the performance of several airfoils for the blades of the impeller of an assist heart pump in order to optimize the performance and efficiency of the pump and reduce blood damages.
Medical Ultrasound / Diagnostic Sonography / Ultrasonography
Mahsa Arab; Ali Fallah; Saeid Rashidi; Maryam Mehdizadeh Dastjerdi; Nasrin Ahmadinejad
Volume 17, Issue 2 , September 2023, , Pages 140-150
Abstract
Breast cancer stands as the most prevalent form of cancer among women, with over 80% of early-stage breast abnormalities being benign. Timely detection is therefore crucial for prompt intervention. Ultrasound Radio Frequency (US RF) signals represent a non-invasive, and real-time screening method for ...
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Breast cancer stands as the most prevalent form of cancer among women, with over 80% of early-stage breast abnormalities being benign. Timely detection is therefore crucial for prompt intervention. Ultrasound Radio Frequency (US RF) signals represent a non-invasive, and real-time screening method for breast cancer, offering advantages in tissue differentiation and cost-effectiveness without requiring additional equipment. This research aims to present an intelligent approach for the classification of benign, suspicious, and malignant breast lesions based on effective features extracted from the time series. The dataset, registered as USRFTS, comprises 170 instances recorded from 88 patients. The proposed methodology encompasses four key phases: pre-processing, feature extraction, feature selection, and classification. In the pre-processing phase, B-mode images are reconstructed from US RF time series, and a radiologist manually selects the Region of Interest (ROI) in each image. Subsequently, diverse features in the time and frequency domains are extracted from each ROI during the feature extraction stage. The ant colony method is employed for the selection of impactful features. The dataset is then subjected to evaluation using classifiers such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Decision Tree (DT), Linear Discriminant Analysis (LDA), and a reference classification method (RCM). The results demonstrate a maximum classification accuracy of 94.95% for two classes and 93.33% for three classes
Biomedical Image Processing / Medical Image Processing
Jamal Esmaeilpour; Sattar Mirzakouchaki; Jalil Seyfali Harsini; Abdorrahim Kadkhoda Mohammadi
Volume 1, Issue 3 , June 2007, , Pages 167-176
Abstract
In this paper, the role of Vector Quantizer Neural Network in classification of six types of ECG signals has been investigated using the features that extracted from Daubechies6 Wavelet transformation. The six types of signals are: normal beat, left bundle branch block beat, right bundle branch block ...
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In this paper, the role of Vector Quantizer Neural Network in classification of six types of ECG signals has been investigated using the features that extracted from Daubechies6 Wavelet transformation. The six types of signals are: normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction paced beat and fusion of paced and normal beats. The required data were obtained from the MIT/BIH arrhythmia databases. By using the annotation files of the databases, the patterns of these six types of ECG signals were separated. Then, for better feature extraction, filtering and scaling on the patterns were applied. We used the energies of the last five detailed signals obtained from the exerting the Wavelet transformation in six levels, as the pattern features for Vector Quantizer Network training and testing. From each class, five hundred patterns were used for network training and one hundred patterns for testing. The results indicated %93.1 accuracy for six classes and above %94.3 for lesser than six classes. Then the rate of similarity and dissimilarity of the classes were considered. Finally, the results of this method were compared with some other methods in terms of accuracy.
Biomimetics
Behzad Seyfi; Hosein Mansourinejad; Bahman Vahidi; Nasser Fatouraee
Volume 6, Issue 3 , June 2012, , Pages 169-175
Abstract
Peristaltic flow is one of the important mechanisms of fluid transmission. In addition to the divers engineering applications, this mechanism plays an important role in biological organs such as digestion system and urine excretion. In this paper, urine bolus transportation in ureter has been investigated ...
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Peristaltic flow is one of the important mechanisms of fluid transmission. In addition to the divers engineering applications, this mechanism plays an important role in biological organs such as digestion system and urine excretion. In this paper, urine bolus transportation in ureter has been investigated experimentally using a peristaltic flow simulator apparatus. Some of the features of this apparatus worth mentioning are its ability to use it to investigate the influence of some important parameters in peristaltic flow, such as the effect of pressure difference between the kidney and the bladder on the quantity of discharge and reflux rates, effect of the mean velocity of bolus transport on discharge rate, existence of fluid film and its effect on bolus discharge rate, and effect of fluid bolus length on reflux rate. Then we compare the obtained results with the similar theoretical studies. It was observed that an increase in the pressure difference between inlet and outlet decreases the ratio of reflux to initial volume of the bolus, and it increases the discharge rate. Moreover, the quantities of reflux and discharge rate decrease by decreasing the bolus transport velocity. It was also observed that the thickness of the fluid film has an inverse relation with respect to the discharge rate and with increasing the bolus length reflux is increasing.
Gait Analysis
Mohsen Sadeghi Mehr; Davoud Naderi; Nader Farahpour; Saeed Davoud Abadi Farahani
Volume 3, Issue 3 , June 2009, , Pages 179-187
Abstract
The present study was devoted to determine the standing human body reactions to perturbation of a base plate in the frontal plane, in order to preserve its stability. A base plate with sinusoidal fluctuation was designed and built and then markers were mounted on the specified locations on it and the ...
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The present study was devoted to determine the standing human body reactions to perturbation of a base plate in the frontal plane, in order to preserve its stability. A base plate with sinusoidal fluctuation was designed and built and then markers were mounted on the specified locations on it and the skin of subjects before testing. During testing the subjects (N=10) tried to preserve their stability against perturbations. By using Motion Analysis System, the body responses of subjects were analyzed. Using inverse dynamic methods and experimental kinematic results, forces and moments applied to the joints and between feet soles and the base plate were determined. In theoretical study, the kinematic and dynamic equations of motion of a robotic model of human body in frontal plane by using repetitive Newton-Euler method were obtained. Based on the stability of the model and supporting vertical forces criterion an object function was defined, in order to assure the stability of the model. By optimization of the object function, angle of the model joints under perturbation and its first and second derivatives were determined. The good agreement of the theoretical and experimental results states that in similar conditions a robotic model can be used instead of expensive and time-consuming experiments.
Biomedical Image Processing / Medical Image Processing
Azar Tolouee; Hamid Abrishami Moghaddam; Masoume Giti
Volume 2, Issue 3 , June 2008, , Pages 179-189
Abstract
Automatic classification of lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) is an important stage in the construction of a computer-aided diagnosis system. In this study, classification of Jung tissue patterns was conducted ...
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Automatic classification of lung tissue patterns in high-resolution computed tomography (HRCT) images of patients affected with interstitial lung diseases (ILD) is an important stage in the construction of a computer-aided diagnosis system. In this study, classification of Jung tissue patterns was conducted using a new machine learning approach. The proposed system comprises three stages. In the first stage, the parenchyma region in HRCT lung images is separated using a set of thresholding, filtering and morphological operators. In the second stage, two sets of overcomplete wavelet filters, namely discrete wavelet frames and rotated wavelet frames are utilized to extract the features from the defined regions of interest (ROJs) within parenchyma. Then, in the third stage, the fuzzy k-nearest neighbor algorithm is employed to perform the pattern classification. Our experiments in lung pattern classification were rendered on four different lung tissue patterns (ground glass, honey combing, reticular, and normal) selected from a database of 340 images from 17 subjects. After applying the technique to classify these patterns in small ROis, we extended the classification scheme to the whole lung in order to produce the quantitative scores of abnormalities in lung parenchyma of the patients. The performance of the proposed method was compared with two state-of-the-art computer based methods for lung tissue characterization. It was also validated against the experienced observers. The average kappa statistic of agreement between two radiologists and the computer was found to be 0.6543 where as the average kappa statistic for the interobserver agreement was 0.6848. This computer system can approach the performance of the expert observers in the diagnosing regions of interest and can help to produce objective measures of abnormal patterns in lung HRCT images.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohammad Rashidi; Hamid Behnam; Ali Sheikhani; Mohammad Reza Mohammadi; Maryam Norouzian
Volume 4, Issue 3 , June 2010, , Pages 187-194
Abstract
This paper presents ICA analysis application for detection of autism disorder. In the first step, resources of EEG signals were extracted by ICA and then time domain and frequency domain processing were implemented. EEG signals of ten children with autism and ten healthy children aged 6 to 11 years have ...
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This paper presents ICA analysis application for detection of autism disorder. In the first step, resources of EEG signals were extracted by ICA and then time domain and frequency domain processing were implemented. EEG signals of ten children with autism and ten healthy children aged 6 to 11 years have been obtained. The results have been compared statistically by T-test. Lower correlation levels between resources of the left hemisphere of the brain especially C3 channel region in autistic children compared with healthy subjects have been observed. Also the average energy of theta frequency band in C3 and F3 channels for children with autism were lower than that in healthy people and this criterion was higher in gamma frequency band.
Farnaz Saberpour; Mohsen Parto Dezfouli; Vahid Shalchyan; Mohammad Reza Daliri
Volume 12, Issue 3 , November 2018, , Pages 189-198
Abstract
Neural adaptation is a brain ability which reduces the neural activities in response to a repeated stimulus. In this study, we examined the effect of adaptation on neural decoding. For this purpose, pure tones with different frequency-amplitude combinations were presented randomly in two sequences (usual ...
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Neural adaptation is a brain ability which reduces the neural activities in response to a repeated stimulus. In this study, we examined the effect of adaptation on neural decoding. For this purpose, pure tones with different frequency-amplitude combinations were presented randomly in two sequences (usual and adaptive). During the task, local field potential (LFP) signals were recorded from the primary auditory cortex of fifteen anesthetized rats. In the usual sequence, the stimuli were presented randomly with 50 ms duration and 300 ms interstimulus interval (ISI). Each combination was presented about 25 times. In the adaptive sequence, same as the usual one, stimuli were presented with this difference that one specific frequency (adapter) with the probability of 80% was presented frequently in this sequence. Comparison between decoding accuracy of two sequences allows us to study the effect of adaptation to a specific frequency on neural decoding. First, considering the power spectrum feature in six frequency bands and using LDA (linear discrimination analysis) classifier, the average decoding accuracy of all frequency-pairs were calculated in the usual sequence. Subsequently, the decoding accuracy of frequency-pairs in the adaptive sequence was calculated and compared with the usual sequence. Results show a significant decoding accuracy between different frequency-pairs in beta, gamma, and high-gamma bands (>12 Hz) of local field potential with an accuracy of about 80%. Moreover, we found that adaptation to one frequency of sound decreases the decoding accuracy of neighbor frequencies. This signature was observed in high-frequency gamma and high-gamma activities (30-120 Hz) of LFPs.
Nano-Biomaterials
Melika Iloukhani; Mohammad Rabiee; Mahvash Oskoui; Fathollah Moztarzadeh; Mahdis Shayan
Volume 5, Issue 3 , June 2011, , Pages 193-204
Abstract
In recent years, nanoparticles have attracted considerable attention due to their special optical, chemical, and electrical properties. Developments of nanoparticles synthesis methods for producing materials with precise size and morphology have been considered recently. Among these methods, biosynthesis ...
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In recent years, nanoparticles have attracted considerable attention due to their special optical, chemical, and electrical properties. Developments of nanoparticles synthesis methods for producing materials with precise size and morphology have been considered recently. Among these methods, biosynthesis has a special position for its high compatibility with environment. The use of microorganism in nanotechnology is one of the important aspects of this issue. In this survey we have used Escherichia coli 35218 to Cadmium Sulfide nanoparticles synthesis. First, appropriate time of cadmium ions addition and their maximum concentrations were determined that they dont inhibit bacterial growth. Then we studied intra and extracellular biosynthesis. According to this survey, this strain wasn't able to produce cadmium sulfide nanoparticles intracellulary but also these nanoparticles were extracellulary synthesized in the medium supplemented with L-cysteine. Formation of CdS nanoparticles, their morphologies and fluorescence properties were determined with WDX, SEM and fluorescence microscopy.
Saeed Hesaraki; Masoud Hafezi Ardakani; Kolsoum Rajabi Monavar; Hosein Mohammadi
Volume 7, Issue 3 , June 2013, , Pages 201-207
Abstract
In this research, effect of temperature and calcium to phosphorus (Ca/P) ratio of raw materials on the type and the amounts of formed phases were investigated by solid state method. Calcium carbonate and dicalcium phosphate were provided as raw materials and mixed with different percentages in a way ...
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In this research, effect of temperature and calcium to phosphorus (Ca/P) ratio of raw materials on the type and the amounts of formed phases were investigated by solid state method. Calcium carbonate and dicalcium phosphate were provided as raw materials and mixed with different percentages in a way that final (Ca/P) ratio was between 1.50 to 1.67 in different batches. Then each of these mixtures was heated at temperatures of 800 to 1200 ° C for 3 hours. Phases were identified with XRD technique and quantitative assessment of phases was evaluated by RIR method and Chung relation. Results showed that in all relations and desired temperatures sintered below 1100 ° C beta tri-calcium phosphate is the dominant phase and hydroxyapatite present as second phase in the composition. In samples which sintered at 1200° C, beta TCP is transferred significantly to alpha TCP. In samples with Ca/p ratio: 1.62, 1.67, hydroxyapatite is dominant phase at 1200° C.
Sobhan Sheykhivand; Sehraneh Ghaemi
Volume 13, Issue 3 , October 2019, , Pages 209-222
Abstract
The automatic classification of sleep stages is essential for the timely detection of disorders and sleep-related studies. In this paper, a single-channel EEG-based algorithm is used to automatically identify sleep stages using discrete wavelet transform and a hybrid model of ant colony optimizer and ...
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The automatic classification of sleep stages is essential for the timely detection of disorders and sleep-related studies. In this paper, a single-channel EEG-based algorithm is used to automatically identify sleep stages using discrete wavelet transform and a hybrid model of ant colony optimizer and neural network based on RUSBoost. The signal is decomposed using a discrete wavelet transform into four levels and statistical properties of each level are calculated. To optimize and reduce the dimensions of feature vectors, hybrid model of ant colony optimizer algorithm and multi-layered neural network are used. Then ANOVA test is applied to validate the selected features. Finally, the classification is performed on RUSBoost, which provides an average of 90% classification accuracy for 2 to 6-class classification of different steps of sleep EEG. Suggesting that the proposed method has a higher degree of success in classifying sleep stages compared to the existing methods.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Zahra Tabanfar; Seyed Mohammad Firouzabadi; Zeynab Shankaei; Giv Sharifi; Kambiz Novin; Anahita Zoghi
Volume 10, Issue 3 , October 2016, , Pages 211-221
Abstract
In this research, we analyzed the EEG signals of patients with brain tumor and healthy participants in order to study the effects of brain tumor on brain signals and also the feasibility of brain tumor detection using EEG signals. For this reason, EEG signals of four channel F3, F4, T3 and T4 from 5 ...
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In this research, we analyzed the EEG signals of patients with brain tumor and healthy participants in order to study the effects of brain tumor on brain signals and also the feasibility of brain tumor detection using EEG signals. For this reason, EEG signals of four channel F3, F4, T3 and T4 from 5 patients with brain tumor and 4 healthy participants were recorded. After preprocessing, linear features in time and frequency domains and nonlinear ones such as fractal dimensions and entropies were extracted. Afterwards, the differentiation between2 groups was analyzed using Davies-Bouldin Index, LDA, KNN and SVM classifiers. According to the results of Davies-Bouldin Index, RMS, Theta Absolute Power, Approximate Entropy and Sample Entropy features in resting state with eyes closed and RMS and Theta Absolute Power features in resting state with eyes opened, had the most distinction between the two groups. In this stage classification of two groups using single features was done and the most accuracy of 88.89% was obtained for RMS feature in resting state with eyes closed. At the end, classification of two groups using all selected features was conducted and the maximum accuracy of 82.54% was obtained for RMS, Theta Absolute Power, Approximate Entropy and Sample Entropy features in resting state with eyes closed. According to the results, EEG linear features have a good capability of detecting brain tumor. As these features are simple and have low computational complexity, they can be used in online applications especially for periodic screening tests.
Tissue Engineering
Shahryar Ramezani Bajgiran; Maryam Saadatmand
Volume 11, Issue 3 , September 2017, , Pages 211-218
Abstract
Despite the advancements made in the tissue engineering, one of the obstacles in producing thick tissues is the means of oxygen transport to the deep layered cells of the engineered tissue and creating the network of veins inside the tissue. One way to overcome this problem is to create a microfluidic ...
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Despite the advancements made in the tissue engineering, one of the obstacles in producing thick tissues is the means of oxygen transport to the deep layered cells of the engineered tissue and creating the network of veins inside the tissue. One way to overcome this problem is to create a microfluidic network of channels inside the porous scaffold. These channels can both enhance the oxygenation and produce a mold for the natural vessels created by the angiogenesis cells. In this paper the dissolved oxygen distribution inside a 2D scaffold, which contains bifurcation based microfluidic channels, has been simulated by the means of computational fluid dynamics. To achieve this, the liquid flow and oxygen transport equations have been solved with considerations to the boundary conditions and suitable parameters. The oxygen transport has been found for the static scaffold, and the scaffolds made from the 0 order to third order of bifurcation with a bifurcation angle of 45 degrees. The results have shown that the scaffold with the second order of bifurcation has a better oxygen distribution and also more free area for the cell proliferation, which is consistent with the references. Next, the bifurcation angle was reduced to 35 degrees for the second order scaffold which resulted in an increase in the non-hypoxic area. Generally, by designing optimized angle of bifurcation based channels, a significant area can be oxygenated, while there will be sufficient surface available for cell proliferations.
Biomedical Image Processing / Medical Image Processing
Mehdi Delavari; Amir Hosein Foruzan; Ben Vi Chen
Volume 8, Issue 3 , September 2014, , Pages 213-227
Abstract
Statistical Shape Models are used to interpret shapes. They include mean and variance of corresponding points of training shapes. One of the most important challenges in building statistical shape models is to establish correct correspondences among landmarks in a training set. In this paper, the ...
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Statistical Shape Models are used to interpret shapes. They include mean and variance of corresponding points of training shapes. One of the most important challenges in building statistical shape models is to establish correct correspondences among landmarks in a training set. In this paper, the non-rigid CPD (Coherent Point Drift) method is used to find correct correspondences among points. This method uses both Deterministic Annealing and a non-rigid scheme to register two shapes simultaneously. Then, the statistical shape model is built using a rigid transformation. The proposed method is evaluated using Compactness, Generalization ability and Specificity measures. The built model is compared to models created using the ICP (Iterative Closest Point), TPS-RPM (Thin Plate Spline – Robust Point Matching) and MDL (Minimum Descreption Length) methods by these metrics. The results show that the proposed method performs like the MDL regarding Specificity measure (0.21±0.06). The Compactness and Generalization ability measures of the proposed method are very similar to those for the MDL method. The run-time of our proposed method is about 68 seconds which is faster than non-rigid TPS-RPM and MDL approaches (390 and 3600 seconds respectively). Our results are superior to the ICP and TPS-RPM algorithms.
Gait Analysis
Ali Maleki; Elham Hasani
Volume 16, Issue 3 , December 2022, , Pages 217-228
Abstract
Parkinson's disease is a neurodegenerative disease that causes severe movement disorders including bradykinesia, rigidity, and tremors. There is no cure for Parkinson's disease, only the symptoms can be managed. Parkinson's disease is diagnosed using the MDS-UPDRS global grading scale. In this scale, ...
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Parkinson's disease is a neurodegenerative disease that causes severe movement disorders including bradykinesia, rigidity, and tremors. There is no cure for Parkinson's disease, only the symptoms can be managed. Parkinson's disease is diagnosed using the MDS-UPDRS global grading scale. In this scale, four levels including slight, mild, moderate, and severe levels are defined for the disease. Recurrence plots and RQA features are tools for describing the behavior of chaotic systems and revealing hidden patterns in system dynamics. In this paper, the effect of Parkinson's disease progression on RQA chaotic features is studied. For this purpose, the dataset of the accelerometer mounted on the hand during the finger tapping test was used, which included 67 healthy data, 54 level one data, 66 level two data, 59 level three data, and 14 level four data. After pre-processing, the recurrence plots of the data were drawn and their RQA characteristics were calculated. Patterns of recurrence plots including separate recurrence points, diagonal lines, vertical lines, black squares, and horizontal and vertical white bands were investigated. According to the obtained results, the patterns of recurrence plots had significant differences among different levels of Parkinson's disease. Therefore, RQA features can be used to automatically determine the level of Parkinson's disease.
Fluid-Structure Interaction in Biological Media / FSI
Saeid Siri; Malikeh Nabaei; Nasser Fatouraee
Volume 9, Issue 3 , December 2015, , Pages 229-241
Abstract
Every organ has its own metabolic and functional requirements and needs a variable amount of blood; hence, autoregulation is an important phenomenon. Shear stress induced autoregulation is defined as the innate ability of an organ to keep its hemodynamic conditions stable against changes in heart rate ...
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Every organ has its own metabolic and functional requirements and needs a variable amount of blood; hence, autoregulation is an important phenomenon. Shear stress induced autoregulation is defined as the innate ability of an organ to keep its hemodynamic conditions stable against changes in heart rate and perfusion pressure. For example, when heart rate changes arterial vessels undergo vasodilation or vasoconstriction in order to stabilize the hemodynamic forces and stresses with respect to the flow needed. The current study examines the local mechanisms employed in automatic control. Local regulatory mechanisms function independently of external control mechanisms, such as sympathetic nerves and endocrine hormones. Therefore, they can be considered isolated mechanisms. The application of boundary conditions in numerical modeling is of utmost importance, hence, using arterial tree modeling to achieve appropriate boundary conditions seems necessary. Thus, we have presented a zero-dimensional (lumped parameter) extensive model first. Then, we used this model to achieve boundary conditions for the common carotid artery. As one of the most important hemodynamic parameters, shear stress regulation will then be modeled in an axisymmetric model of this artery.
Speech processing
Shahla Azizi; Farzad Towhidkhah; Farshad Almasganj
Volume 6, Issue 4 , June 2012, , Pages 257-265
Abstract
In present work, recognition of isolated word has been studied. The purpose of this research is to increase the performance of children’s speech recognizer using Vocal Tract Length Normalization. This recognition system has been created to design a speech therapy software. Recognition of correct ...
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In present work, recognition of isolated word has been studied. The purpose of this research is to increase the performance of children’s speech recognizer using Vocal Tract Length Normalization. This recognition system has been created to design a speech therapy software. Recognition of correct and wrong pronunciation and help children to improve it using some feedbacks are the goals of this software. In test phase, some speech data that are related to correct and incorrect pronunciation of 47 words have been utilized. Four Baseline models have been Trained, one for children, one combined model (females and children) and two for Adults (by exploiting one Persian database). Children’s model was trained and tested with data that have been collected from 38 children (5 to 8 years old). These experiments were implemented in HTK toolkit. Poor performance was improved using VTLN. Improvement of adult’s model was more than children’s model.
Biomechanical Motor Control / Motor Control of Human Movement
Saeed Rashidi; Ali Fallah; Farzad Towhidkhah
Volume 1, Issue 4 , June 2007, , Pages 269-280
Abstract
Dynamic signature verification based on temporal features are more precise than the static methods because in addition to position information of the drawing pattern, it uses local and global features extracted from velocity, acceleration, pressure and pen angle signals, while static methods only use ...
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Dynamic signature verification based on temporal features are more precise than the static methods because in addition to position information of the drawing pattern, it uses local and global features extracted from velocity, acceleration, pressure and pen angle signals, while static methods only use image information. In this study, we segmented the signature patterns using the basic role of velocity in the control process of skilled movements and then the function features were extracted. In order to signal the matching evaluation, we applied five generalized functions and five weighting strategies for score level fusion. The results showed that the correlation criterion had the minimum error. The experiments on the database, consisting of persons of Persian, Chinese and English, showed that the skilled forgeries obtained an equal error rate (EER) of 0.87% and 1.24% for the user and universal thresholds, respectively.
Biomechanics of Bone / Bone Biomechanics
Mahmoud Azami; Fathollah Moztarzadeh; Mohammad Rabiee
Volume 3, Issue 4 , June 2009, , Pages 275-284
Abstract
During past decade, using biomimetic approaches has received much attention by scientists in the field of tissue substitutes preparation. These approaches have been employed for synthesis of bone tissue engineering scaffolds in the case of either materials or synthesis methods. In this study, an apatite ...
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During past decade, using biomimetic approaches has received much attention by scientists in the field of tissue substitutes preparation. These approaches have been employed for synthesis of bone tissue engineering scaffolds in the case of either materials or synthesis methods. In this study, an apatite phase has been synthesized within gelatin hydrogel in biomimetic condition. The obtained composite hydrogel has changed to a porous scaffold with the application of freeze drying technique in order to be used in bone tissue engineering. To characterize the chemical composition and crystal structure of the synthesized precipitate within hydrogel, FTIR, XRD and TEM analysis were used. Surface morphology and porous structure of the scaffold were studied with SEM. SEM analysis was also used to investigate the quality of cultured osteoblast cells activity. Results approved formation of an apatite phase within gelatin hydrogel in biomimetic condition with crystallite size ranging between 7-10 nm. Porosity percentage of the obtained nanocomposite scaffold was about 82% with pores sizes in the range of 100-350μm. Young’s elastic modulus of the scaffold was comparable with that of the spongy bone. The osteoblast cells cultured on the scaffold showed adhesion, immigration and extracellular matrix excretion on the scaffold internal surfaces. Thus, obtained results indicated the potential ability of the prepared biomimetic bone tissue engineering scaffold to be used in bone tissue repair process.
Neuro-Muscular Engineering
Sahar Akbari; Vahid Shalchyan; Mohammad Reza Daliri
Volume 12, Issue 4 , January 2019, , Pages 277-286
Abstract
Neural spike detection is the first step in the analysis of neural action potentials in extracellular recordings. The background noise which mainly originates from a large number of far neuronal units, usually confront with detection of low-amplitude spikes. So far, many scholars have devoted their works ...
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Neural spike detection is the first step in the analysis of neural action potentials in extracellular recordings. The background noise which mainly originates from a large number of far neuronal units, usually confront with detection of low-amplitude spikes. So far, many scholars have devoted their works to this subject and many algorithms have been proposed. In this paper we present an automatic spike detection algorithm for the noise-contaminated extracellular signal. This algorithm consists of four steps: 1- A bandpass filtering and using a differential filter; 2- applying Shannon's energy nonlinear filter; 3- Hilbert transform; and 4- Thresholding of the signal. The proposed method has been compared with five known methods in spike detection. This comparison is done on two simulated datasets and one real data set. The results indicate the superiority of the proposed method for simulated data compared to other methods, which indicates the robustness of the proposed algorithm to the noise. Meanwhile, for real data, it reaches the second place among all six methods. Using Shannon's non-linear energy filter can be an effective way to detect spikes in extracellular signal recordings. The comparison indicates that this method is superior to the commonly known methods for spike detection.
Medical Robotics / Bio-Robotics
Elaheh Kafashi; Mohammad Ali Ahmadi Pajouh; Firooz Bakhtiari Nejad
Volume 14, Issue 4 , February 2021, , Pages 277-290
Abstract
Due to the high number of patients with cerebrovascular disease and stroke, which results in paralysis of organs on one side of the body, including the hand, as well as limitations in traditional rehabilitation methods, it is necessary to build devices to help these people. In this study, initially, ...
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Due to the high number of patients with cerebrovascular disease and stroke, which results in paralysis of organs on one side of the body, including the hand, as well as limitations in traditional rehabilitation methods, it is necessary to build devices to help these people. In this study, initially, given the challenges involved in designing an exoskeleton, the initial design was a mechanism for using it as a continuous passive motion to rehabilitate the fingers. This mechanism is tendon-based and covers both the flexion and extension of the fingers. For this purpose, two active and passive actuators have been used in the exoskeleton, respectively, to flex and extend the fingers. The distinctive feature of this design is its lightness, low volume, adjustability for different hands, compatibility, and comfort for the patient. Also, the kinematics and dynamics relationships modeled on the Lagrange method. The exoskeleton movement simulated in interaction with the finger with MATLAB sim-mechanics software. Finally, using simulation and modeling results, the final design was performed by considering the force of 40 N along the tendon, the exoskeleton made for the index finger. Also, the results of analytical modeling and simulation compared; the error rate of modeling obtained. In the worst case, this value was 15% for the first and second finger joints and 20% for the third joint.
