Dental Biomechanics
Seyed Khatiboleslam Sadrnezhaad; Amir Hossein Tavabi; Saeed Ghoreishi
Volume -1, Issue 2 , June 2005, , Pages 181-191
Abstract
Tooth straightening with superelastic wire requires exertion of continued bending as well as tensional forces exerted by the wires to the teeth. The applied force can influence on properties of the wire. Knowing the amount and mechanism of this change results in both improvement of the clinical operation ...
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Tooth straightening with superelastic wire requires exertion of continued bending as well as tensional forces exerted by the wires to the teeth. The applied force can influence on properties of the wire. Knowing the amount and mechanism of this change results in both improvement of the clinical operation as well as the recovery of the used alloy. Investigating the possibility of exertion of a stable force during the curing period is substantial to orthodontists. Studying the possibility of recovery and re-circulation of the used material is of interest to engineers. The latest results obtained on the effect of bending on transformation temperatures, crystal structure and mechanical properties of four different orthodontic commercial wires are discussed in this paper. It is seen that the width of the hysteresis loop is reduced, percentage of the marten site phase is increased and the possibility of stress induced Rphase formation is increased due to the application of the deflection strains on the samples. The structural phase change occurring during mechanical and/or heating operations indicates that the alloy property can change from superelastic towards shape memory effect via heat treatment after cold working. Microstructural and transformation temperature studies show that R-phase formation is concomitant with the presence of marten site in the wires. These results indicate that the superelastic effects are correlated to the formation and elimination of small forcible hysteresis loop of the R phase.
Bioelectrics
Marzieh Alirezaei Alavijeh; Ali Maleki
Volume 10, Issue 2 , August 2016, , Pages 187-196
Abstract
Brain-computer interface system based on Steady-state visual evoked potentials is taken into consideration due to advantages such as simplicity of installation and use of the system, enough accurate and acceptable Information transfer rate. In addition to these benefits, short processing time is also ...
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Brain-computer interface system based on Steady-state visual evoked potentials is taken into consideration due to advantages such as simplicity of installation and use of the system, enough accurate and acceptable Information transfer rate. In addition to these benefits, short processing time is also an important criterion to have a system that is applicable in real life and have the ability to use online. In this paper, a method based on standard CCA have been present for recognition of stimulus frequency. The proposed method is performed in two stages, offline and online. In the offline stage, the standard CCA is applied to the SSVEP and sin-cos reference signals. After that, template signals are constructed using weights that generate maximum correlation. In online stage, cross correlation between test signal and each template signals are calculated and the stimulus frequency is recognized. The greater accuracy of frequency recognition and less calculation time at the same time are shown by stimulation result.
Zahra sadat Hosseini; Mohadese Arabgari; Ali Farmad; Leili Goldoozian; Hamid Reza Maghari; Sara Aghajari; Edmond Zahedi
Volume 7, Issue 3 , June 2013, , Pages 277-285
Abstract
In this article a wireless patient monitoring system for vital signs (respiratory rate and heart beat) is presented. The recorded biosignal is the photoplethysmogram using a probe attached to the patient's finger. This signal is amplified, filtered and digitized by an on-board processor unit before finally ...
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In this article a wireless patient monitoring system for vital signs (respiratory rate and heart beat) is presented. The recorded biosignal is the photoplethysmogram using a probe attached to the patient's finger. This signal is amplified, filtered and digitized by an on-board processor unit before finally being sent wirelessly via a transmitter. The capacity of the current system is 16 patients whose data can be received through a common receiver by a central server which measures and displays the heart beat and respiratory rate for each patient on the monitor.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Sanaz Ahmadzadeh; Hamid Reza Kobravi; Saeed Tosizadeh
Volume 8, Issue 3 , September 2014, , Pages 293-304
Abstract
Multiple muscle groups may be activated simultaneously during the most of activities. So, the appropriate muscle coordination must be emerged during a normal activity. Consequaently, for rehabilitation of movements such as hand writing and paiting in patients for example suffering from carpal channel ...
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Multiple muscle groups may be activated simultaneously during the most of activities. So, the appropriate muscle coordination must be emerged during a normal activity. Consequaently, for rehabilitation of movements such as hand writing and paiting in patients for example suffering from carpal channel syndrom or incomplete spinal cord injury, the correct muscle coordination patterns between the finger muscles and wrist muscles must be reestablished. So, in this paper a prediction methodology based on artificial neural networks (ANN) is proposed to approximate the Thumb fingure extensor and flexor muscles desired activation pattern during the hand writing and Painting. In the presented strategy, A nonlinear auto-regressive neural network (NARX), Recurrent Neural Network (RNN), Radial Basis Function (RBF), Multy Layer Perceptron (MLP) and an Adaptive-network-based fuzzy inference system (ANFIS) are trained to forecast the Extensor pollicis longus and Flexor pollicis brevis muscles activity of one thumb finger of hand using Extensor carpi radialis brevis and Flexor carpi ulnaris muscles activity of forearm. Quantitative evaluations show the promising performance of developed neural networks. Eight healthy volunteers participated in the experiments.
Orthotics & Prosthesis
Rouhollah Sameri Nedafi; Ali Moazemi Goudarzi; Alireza Fathi
Volume 9, Issue 3 , December 2015, , Pages 305-316
Abstract
Abstract: The statistical studies indicate that diseases, accidents and wares are the principal causes to increase the number of amputees in the world. These studies also show that the most of mutilation disabilities are related to musculoskeletal. Obesity, sedentary, lack of proper exercise as well ...
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Abstract: The statistical studies indicate that diseases, accidents and wares are the principal causes to increase the number of amputees in the world. These studies also show that the most of mutilation disabilities are related to musculoskeletal. Obesity, sedentary, lack of proper exercise as well as the risk of some diseases, cause weaken in knee muscles and other difficulties of this hand. As a consequence, the knee muscles can`t apply a mighty torque to accomplish knee motion.The objective of this study is to propose a proper solution to improve the life quality of those who suffer from weak knees. In this study, by investigating the biomechanical behavior of a healthy foot in a normal gait, the indispensable power which can enforce a 50% weak Knee to achieve the same gait can be calculated. In order to naturalize the mentioned knee, a new control-active orthosis is designed. The proposed design is specified by an electromechanical actuator and an elastic component articulated in a light weight four-bar mechanism. Its mechanical behavior is tested in a simulated walking gait and the optimum value of elastic coefficient is estimated as 7KN/m. In this case, the maximum torque applicable to knee joint has increased by 34 per cent.
Nasrin Ghazanshahi
Volume 2, Issue 4 , June 2008, , Pages 351-356
Abstract
Peritoneal Dialysis (PD) units treat renal failure, partially replacing kidney function by removing metabolic wastes and fluid through selective diffusion and osmosis across the peritoneum. A prototype of PD unit is designed and made for the first time in IROST. Our unit features are control of filling ...
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Peritoneal Dialysis (PD) units treat renal failure, partially replacing kidney function by removing metabolic wastes and fluid through selective diffusion and osmosis across the peritoneum. A prototype of PD unit is designed and made for the first time in IROST. Our unit features are control of filling dialysate flow to peritoneal cavity and draining from it, warming the dialysate to body temperature and controlling the dialysate temperature before filling, Control and monitoring fill time, dwell time, drain time, number of cycles on the basis of dialysis type, computing the ultra filtration, reporting and storing capability, computer connection capability, programming for individual patient, etc. In this unit, all controls and monitoring of different parameters are based on digital and microprocessor system.
Gait Analysis
Mohammad Iman Mokhlespour Esfahani; Omid Zobeiri; Ali Akbari; Behzad Moshiri; Mohammad Parnianpour
Volume 7, Issue 4 , June 2013, , Pages 361-369
Abstract
Wearable measuring system has major effects onbiomechanics of human movements especially in daily activitiesin order to monitor and analyze the human movements to achievethe most important kinematics parameters. In the recent decade,inertial sensors were utilized by researchers in order todeveloping ...
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Wearable measuring system has major effects onbiomechanics of human movements especially in daily activitiesin order to monitor and analyze the human movements to achievethe most important kinematics parameters. In the recent decade,inertial sensors were utilized by researchers in order todeveloping wearable system for instrumentation of humanmovements. In this study, Sharif-Human MovementInstrumentation System (SHARIF-HMIS) was designed andmanufactured. The system consists of inertial measurement units(IMUs), stretchable clothing and data logger. The IMU sensorsare installed on the human body. The system can be used at homeand also industrial environments. The main features of thissystem are: low cost, low weight, saving data for ten hours andbeing wearable. Furthermore, the software was designed for data acquisition of the IMUs.
Bioelectrics
Saeid Shakeri
Volume 9, Issue 4 , February 2015, , Pages 399-410
Abstract
Falls are one of the main reasons to injury, especially in the elderly people. These injuries can be reduced by quick and accurate response or reaction, but this is not possible often in elderly people because they usually live alone and after injury caused the falling, cannot call for help. This paper ...
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Falls are one of the main reasons to injury, especially in the elderly people. These injuries can be reduced by quick and accurate response or reaction, but this is not possible often in elderly people because they usually live alone and after injury caused the falling, cannot call for help. This paper presents a fall detection system to do twomajor tasks properly and quickly; firstly, it shoulddetect fall from other daily activities andsecondly, transmit falling person’s necessary information to help. This system is implemented on Android-based smartphone and it used tri-axial accelerometer and microphone to fall detection. Everydayinteraction with the smartphone makes our system more familiarto the user. The accelerometer is used to record variations of acceleration in three directions.Thissystem isimproved with detecting the noise caused the falling, by analyzing environmental sounds. After fall detection, a warning text message that contains information about time and location of the falling will besent to the caregivers. A comprehensive evaluation with 18 volunteers shows that the proposed system has sensitivity of 96% and specificity of 77% for different types of fall in quiet and noisy environments.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Afarin Nazemi; Ali Maleki
Volume 8, Issue 4 , February 2015, , Pages 411-420
Abstract
Classification of distal limb movements based on surface electromyography (sEMG) of proximal muscles is an important issue in the control of myoelectric hand prosthesis. In most of previous studies, classification of a limited number of hand motions is investigated. In this paper, we have used NINAPRO ...
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Classification of distal limb movements based on surface electromyography (sEMG) of proximal muscles is an important issue in the control of myoelectric hand prosthesis. In most of previous studies, classification of a limited number of hand motions is investigated. In this paper, we have used NINAPRO database containing kinematics and sEMG of upper limbs while performing 52 finger, hand and wrist movements. We evaluated performance of LDA and LS-SVM with RBF kernel classifiers using different combination of features. First by windowing the signal with two different methods, the major part of the signal was selected and eight various temporal features (MAV, IAV, RMS, WL, E, ER1, ER2, CC) were extracted. Then performance of each classifier with single, double and multiple combinations of features was evaluated. For LDA classifier, the best average classification accuracy of 84.23% was achived for first windowing method and MAV (or IAV)+CC features, The corresponding accuracy for LS-SVM classifier with second windowing method and IAV+MAV+RMS+WL features, was 85.19%.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohammad Ali Manouchehri; Vahid Abootalebi; Amin Mahnam
Volume 9, Issue 2 , July 2015, , Pages 205-214
Abstract
SSVEP-based BCI systems have attracted attention of many researchers due to their high signal to noise ratio, high information transfer rate and being easy for use. The processing goal of these systems is to detect the stimulus frequency of EEG signal. Among the processing methods for frequency identification ...
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SSVEP-based BCI systems have attracted attention of many researchers due to their high signal to noise ratio, high information transfer rate and being easy for use. The processing goal of these systems is to detect the stimulus frequency of EEG signal. Among the processing methods for frequency identification in SSVEP-based BCI systems, LASSO algorithm has gained great acceptance. Although LASSO has acceptable performance in SSVEP-based BCI systems, it doesn't consider the phase of recorded EEG signal for creating the reference signal. In this paper, the idea of correcting the phase of the reference signal with respect to recorded EEG signal was investigated and a new method called phase corrected LASSO was proposed. For this purpose, first, the optimal EEG channel for frequency identification was determined and then, the performance of the phase corrected LASSO method was compared with standard LASSO method. The results show that the phase corrected LASSO method has better performance compared with the standard LASSO method.
Spinal Biomechanics
Mohammad Javad Einafshar; Seyed Ataollah Hashemi; Pedram Mojgani
Volume 14, Issue 3 , October 2020, , Pages 169-177
Abstract
Back pain is a common medical problem. There is no clear cause for the back pain problem so far, but in most cases, spinal instability can be noted. Lumbar spine fixation is performed to treat the problems of low back pain. Spinal fixation can be done with or without surgery. One of the surgical methods ...
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Back pain is a common medical problem. There is no clear cause for the back pain problem so far, but in most cases, spinal instability can be noted. Lumbar spine fixation is performed to treat the problems of low back pain. Spinal fixation can be done with or without surgery. One of the surgical methods is the use of spinal screws in which the strength and stability of the screw are of great importance. The strength and stability of the screw in the bone reduces the time and cost of treatment, reduces the amount of bleeding and accelerates the patient's treatment. In this study, screws were inserted using a digital torque meter. An impact was applied using an impact hammer and resonated sound was recorded using a microphone. The vibration mode of the screw was obtained by processing the signal generated by MATLAB R2017 software and plotting the fast Fourier transform. Finally, tensile test was performed to obtain the ultimate pull-out force. The innovation of this study was to use modal analysis method and to correlate its results with that of the ultimate pull-out force and peak insertion torque. In this study, five screws with different screw depth, and screw thread crest thickness were examined. Also, the effect of self-tapping was investigated. The peak insertion torque, ultimate pull-out strength and natural frequency occurred at 182 Nm, 992 N and 1916 Hz, respectively, for the cylindrical pedicle screw. By comparing the obtained data, results showed a linear relationship between insertion torque and pull-out force of the screws. Due to the lack of significant difference between natural frequency and pull-out force of the self-drilling and non-self-drilling tip screws (comparing between screws number 3 and 4 and between 1 and 5), the use of self-tapping screws can be advantageous. The trend of the dependent parameters in all three methods i.e. insertion torque, pull-out force and natural frequency are the same, indicating the non-destructive advantage of modal analysis in in-vivo surgical application.
Bioelectrics
Sobhan Sheykhivand; Zohreh Mousavi; Tohid Yousefi Rezaii
Volume 14, Issue 3 , October 2020, , Pages 179-193
Abstract
In recent years, driver fatigue has become one of the major causes of road accidents, and many studies have been conducted to analyze driver fatigue. EEG signals are considered the most reliable method for measuring driver fatigue because of the non-invasive nature. Manual interpretation of EEG signals ...
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In recent years, driver fatigue has become one of the major causes of road accidents, and many studies have been conducted to analyze driver fatigue. EEG signals are considered the most reliable method for measuring driver fatigue because of the non-invasive nature. Manual interpretation of EEG signals for detection of driver fatigue is impossible, so an automatic detection of driver fatigue from EEG signals should be provided. One of the problems regarding the automatic detection of driver fatigue is extraction and selection of discriminative features witch generally leads to computational complexity. This paper prepares a new approach to automatic classifying 2 stages of driver fatigue from 6 active regions of EEG signals. In the proposed method, directly apply the raw EEG signal to convolutional neural network-long short time memory (CNN-LSTM) network, without involving feature extraction/selection. This is a challenging process in previous literature. The proposed network architecture includes 7 convolutional layers with 3 LSTM layers followed by 2 fully connected layers. The LSTM network in a fusion with the CNN network has been used to increase stability and reduce oscillation. The simulation results of the proposed method for classifying 2 stages of driver fatigue for 6 active regions A, B, C, D, E (based single-channel) and F show the accuracy of 99.23%, 97.55%, 98.00%, 97.26%, 98.78%, 93.77% and Cohen’s Kappa coefficient of 0.98, 0.96, 0.97, 0.96, 0.98 and 0.92 respectively. Furthermore, comparing the obtained results with the previous methods reveals the performance improvement of the proposed driver fatigue detection in terms of accuracy. According to the high accuracy of the proposed single-channel (region E) method, it can be used for the design of automatic detection of driver fatigue systems with high speed and accuracy.
Medical Ultrasound / Diagnostic Sonography / Ultrasonography
Saba Jaafari Kia; Hamid Behnam; Majid Vafaeezadeh; Ali Hosseinsabet
Volume 15, Issue 3 , December 2021, , Pages 187-197
Abstract
Heart diseases are main cause factors endangering human health and life, one of the most important heart diseases is valvular heart disease, which has had an increasing trend in recent years. Therefore, if they are diagnosed and treated in time and correctly, they can improve the quality of life and ...
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Heart diseases are main cause factors endangering human health and life, one of the most important heart diseases is valvular heart disease, which has had an increasing trend in recent years. Therefore, if they are diagnosed and treated in time and correctly, they can improve the quality of life and increase the life expectancy, so researchers have always been looking for ways to improve and accelerate the process of diagnosing this disease. Medical images monitoring and recording the activity of the human heart are the main ways to diagnose heart diseases. Processing of these images is generally complex and time consuming, so scientists and experts have always been looking for ways to speed up and facilitate the detection process. Manifold learning is one of the nonlinear dimension reduction methods which has different algorithms and can simplify the processing of echocardiographic images. In this study, using one of the manifold learning algorithms named LLE, we examined echocardiographic images of the heart, and tried to categorize groups with mitral disorders while identifying healthy data from those with disorders. Results show that the method has carefully separated the data of the healthy group from the group with the disorder, and good results were obtained in the data classification. The results show that more than 80% of the samples of the natural group have a different pattern in terms of manifold structure from the samples with the disorder.
Khosro Rezaee; Fardin Ghaderi; Hamed Taheri Gorji; Javad Haddadnia
Volume 14, Issue 3 , October 2020, , Pages 195-208
Abstract
In modern prostheses, accurate processing of surface electromyogram (sEMG) signals has a significant effect on optimal muscle control. Although these signals are useful for diagnosing neuromuscular diseases, controlling prosthetic devices and detecting hand movements, non-robustness of EMG signal-based ...
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In modern prostheses, accurate processing of surface electromyogram (sEMG) signals has a significant effect on optimal muscle control. Although these signals are useful for diagnosing neuromuscular diseases, controlling prosthetic devices and detecting hand movements, non-robustness of EMG signal-based recognition will give rise to various movement disorders. In this paper, we present an optimal approach to classify EMG signals for hand gesture and movement recognition, whose purpose is to be used as an efficient method of diagnosing neuromuscular diseases, determining the type of treatment and physiotherapy. The main assumption of this study is to improve the accuracy of recognition and therefore, we proposed a novel hand gesture and movement recognition model consists of three steps: (1) EMG signal features extraction based on time-frequency domain and fractal dimension features; (2) feature selection by soft ensembling of three procedures in which includes two sample T-tests, entropy and common wrapper feature reduction, and (3) classification based on kernel parameters optimization of SVM classifier by using Gases Brownian Motion Optimization (GBMO) algorithm. Two UC2018 DualMyo and UCI datasets have been considered to evaluate the proposed model. The first dataset is used to classify eight hand gestures and the second dataset is employed for the classification of six types of movement. The experiment results and statistical tests reveal that the designed approach has desirable performance with an average accuracy of above 98% in both datasets. Contrary to similar methods that perform classifications in finite classes with high error rates, the integrated method has satisfactory accuracy, robustness and reliability. Not only the proposed method contributes to the design of prostheses, but also provides effective outcomes for rehabilitation applications and clinical diagnosis processes.
Neural Network / Biological & Artificial Neural Network / BNN & ANN
Hossein Banki-Koshki; Seyyed Ali Seyyedsalehi
Volume 15, Issue 3 , December 2021, , Pages 199-209
Abstract
The presentation of new neuronal models to simulate cognitive phenomena in the brain has attracted the research interests in recent years. In this study, a new neural model based on the chaotic behavior of weights of artificial neural networks during training by back-propagation algorithm is presented. ...
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The presentation of new neuronal models to simulate cognitive phenomena in the brain has attracted the research interests in recent years. In this study, a new neural model based on the chaotic behavior of weights of artificial neural networks during training by back-propagation algorithm is presented. This model is the first discrete neuronal model with learning ability and shows complex and chaotic behaviors. The learning ability of this model has enabled it to simulate cognitive phenomena such as neuronal synchronization in near-realistic conditions. The model, which is derived from a simple three-layered feed-forward neural network, has several coexisting attractors that make learning possible in various basins of attraction. The study of model parameters shows that bifurcation occurs not only by changing the learning rate, but also external stimulation can change the model behavior and bifurcation pattern. This point that can be used in modeling and designing new therapies for cognitive disorders.
Fluid-Structure Interaction in Biological Media / FSI
Alireza Hashemifard; Nasser Fatouraee; Malikeh Nabaei
Volume 17, Issue 3 , December 2023, , Pages 201-210
Abstract
The crucial responsibility of the aortic valve is to prevent returning of blood flow from the aorta back to the left ventricle. In-time and accurate opening and closing of the aortic valve can effectively produce the desired blood pressure and cardiac output. For this reason, aortic valve simulation ...
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The crucial responsibility of the aortic valve is to prevent returning of blood flow from the aorta back to the left ventricle. In-time and accurate opening and closing of the aortic valve can effectively produce the desired blood pressure and cardiac output. For this reason, aortic valve simulation can identify changes related to aortic valve hemodynamics and their relationship. Diagrams of the left ventricular pressure, the left ventricular pressure difference relative to the aortic artery, GOA, blood flow, the left ventricle pressure-to-volume, the left ventricular energy, kinematic energy density, viscous dissipation, valve resistance, fluid pressure difference in two The surface side of the leaflets, and the momentary pressure difference of the longitudinal axis of the aortic valve compared to the pressure of the aortic artery are reported in this research and based on these, the process of opening and closing of the aortic valve is analyzed using numerical methods named ALE. The moving of the aortic leaflet as the displacement of the solid boundary in the fluid-solid interaction method causes the fluid mesh to undergo displacement and change, which is repaired by the sequence of re-meshing in the fluid domain. In this process, problems occur, the details of which and the resolving method are explained in detail.
Bioelectrics
Sobhan Sheykhivand; Zohreh Mousavi; Tohid Yousefi Rezaii
Volume 14, Issue 3 , October 2020, , Pages 209-220
Abstract
Using a smart method to automatically detect different stages of epilepsy in medical applications, to reduce the workload of physicians in analyzing epilepsy data by visual inspection is one of the major challenges in recent years. One of the problems of automatic identification of different stages of ...
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Using a smart method to automatically detect different stages of epilepsy in medical applications, to reduce the workload of physicians in analyzing epilepsy data by visual inspection is one of the major challenges in recent years. One of the problems of automatic identification of different stages of epilepsy is extraction of desirable features which can make the most distinction between different stages of epilepsy. The process of finding the proper features is generally time consuming. This study presents a new approach for the automatic identification of different epileptic stages. In this paper, a sparse represantion-based classification (SRC) with proposed dictionary learning is used to automatically identify the different stages of epilepsy using the EEG signal. The proposed method achieves 100% accuracy, sensitivity and specificity in 8 out of 9 scenarios. Also the proposed algorithm is resistant to Gaussian noise up to 0 decibels. The results show that using the proposed algorithm to identify different epileptic stages has a higher success rate than other similar methods.
Biomedical Imaging / Medical Imaging
Farzaneh Keyvanfard; Alireza Rahiminasab; Abbas Nasiraei Moghaddam
Volume 15, Issue 3 , December 2021, , Pages 211-220
Abstract
In brain disorders, both the brain structural and functional connectivity are altered and cause different behavioral symptoms. Recognizing these variations can help us to diagnose, treat, and control its progression. Schizophrenia is one of these mental disorders that widely affects the brain structure ...
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In brain disorders, both the brain structural and functional connectivity are altered and cause different behavioral symptoms. Recognizing these variations can help us to diagnose, treat, and control its progression. Schizophrenia is one of these mental disorders that widely affects the brain structure and function. Investigation of brain variations in this disease has commonly been based on voxel-wise analysis or region-based studies. The aim of this study is to evaluate brain structure and function alterations in schizophrenia patients comparing to healthy control from the brain connectivity perspective. For this purpose, using the statistical test method, a comparison was made between all the structural and functional connections in the brain of 92 healthy individuals and 37 schizophrenia patients obtained from diffusion tensor imaging (DTI) and functional Magnetic Resonance Imaging (fMRI) respectively. The findings of this study indicate that the number of altered edges in the brain functional network of patients is about 4 times more than the number of varied structural connections, which indicates the high impact of this disorder on brain function. Also, examination of the number of altered edges connected to each node, the affected areas in this disease were identified and it was shown that the schizophrenia patients’ brain has changed in parts of the brain subnetworks related to the default mode network (DMN), attention, somatomotor and vision networks. It was also shown that the altered brain structural connections of patients are involved in the areas of the superior frontal gyrus, temporal gyrus and part of the occipital cortex which are mostly shown relative increasing of the structural connectivity weights. The results of this study indicate the widespread effect of this disorder on the brain and suggest that the occurrence of some abnormal behaviors in schizophrenia patients may be due to some increased structural connectivity weights.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Aref Einizade; Sepide Hajipour Sardouie
Volume 14, Issue 3 , October 2020, , Pages 221-233
Abstract
The brain electrical signal has been widely used in clinical and academic research, due to its ease of recording, non-invasiveness, and precision. One of the applications can be emotion recognition from the brain's electrical signal. Generally, two types of parameters (Valence and Arousal) are used to ...
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The brain electrical signal has been widely used in clinical and academic research, due to its ease of recording, non-invasiveness, and precision. One of the applications can be emotion recognition from the brain's electrical signal. Generally, two types of parameters (Valence and Arousal) are used to determine the type of emotion which in turn indicate "positive or negative" and "level of extroversion or excitement" for a specific emotion. The significance of emotion is determined by the effects of this phenomenon on daily tasks, especially in cases where the person is confronted with activities that require careful attention and concentration. In the emotion recognition problem, firstly, using proper emotion stimuli, different emotions are created for the subjects under study and the brain signals corresponding to each stimulus are recorded. The two main steps for solving the emotion recognition problem are extracting suitable features and using appropriate classification or regression methods. In previous studies, different visual and auditory have been used and various linear and nonlinear features and classifiers have been investigated. In this paper, the main goal was the improvement of linear regression algorithms to estimate the criteria for recognizing human emotions more efficiently. For this purpose we proposed a new algorithm that uses the sparseness of the mixing vector along with the linear regression cost function. The effectiveness of the proposed algorithm on simulated data has been investigated and its superiority to linear regression algorithms such as PLS, LASSO, SOPLS and Ridge was shown. Also, to apply the proposed algorithm on EEG data corresponding to emotion recognition, the DEAP dataset was used and the AR coefficients were extracted from the EEG signals. The results obtained from the proposed algorithm were compared with those of the other linear regression algorithms, which in total showed the relative superiority of the proposed method.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Hamed Danandeh Hesar; Amin Danandeh Hesar
Volume 15, Issue 3 , December 2021, , Pages 221-234
Abstract
Extended Kalman filter (EKF) is a well-known nonlinear Bayesian framework that has been deployed in various fields of ECG processing. However, it’s not very effective in removing non-stationary noises such as muscle artifacts (MA) which are common in ECG recordings. This paper addresses this issue ...
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Extended Kalman filter (EKF) is a well-known nonlinear Bayesian framework that has been deployed in various fields of ECG processing. However, it’s not very effective in removing non-stationary noises such as muscle artifacts (MA) which are common in ECG recordings. This paper addresses this issue by proposing a new ECG dynamic model (EDM) and a novel formulation for EKF which improves its performance in non-stationary environments. In the new EDM, the measurement model is modified to include non-Gaussian, non-stationary additive noises as well as stationary ones. The proposed formulation for EKF algorithm in this paper enables it to perform better than standard EKF in removing non-stationary contaminants. The proposed filter also preserves the clinical characteristics of ECG signals better than standard EKF. In order to show the effectiveness of the proposed EKF algorithm, its denoising performance was evaluated on MIT-BIH Normal Sinus Rhythm database (NSRDB) in the presence of two different types of non-stationary contaminants; synthetic pink noise and real muscle artifact noise. The results showed that the proposed EKF framework in this paper has a significant outperformance over the standard EKF framework in non-stationary environments from both SNR improvement and MSEWPRD viewpoints.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Hessam Ahmadi; Emad Fatemizadeh; Alimotie Nasrabadi
Volume 14, Issue 3 , October 2020, , Pages 235-249
Abstract
Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging technique for analyzing the brain functions through low-frequency fluctuations called the Blood-Oxygen-Level-Dependent (BOLD) signals. Measurement of the functional connectivity in brain networks is usually done by the fMRI time-series ...
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Functional Magnetic Resonance Imaging (fMRI) is a non-invasive neuroimaging technique for analyzing the brain functions through low-frequency fluctuations called the Blood-Oxygen-Level-Dependent (BOLD) signals. Measurement of the functional connectivity in brain networks is usually done by the fMRI time-series through Pearson Correlation Coefficients (PCC). As the PCC shows linear dependencies, in this study, non-linear relationships in the fMRI signals of the patients with Alzheimer's Disease (AD) were investigated using the kernel trick method. Kernel trick approach maps the input information into a higher dimension space and implements the linear calculations in a new space that is proportionate to the non-linear relationships in the primary space. After generating the weighted undirected brain graphs based on the Automated Anatomical Labeling (AAL) atlas, different kernel functions with different parameters were applied. Then the graph global measures including degree, strength, small-worldness, modularity, and efficiencies features were computed and the non-parametric permutation test was performed. According to the results, the kernel trick method showed more significant differences with AD and healthy subjects in comparison with the simple PCC and it could be because of the non-linear correlations that are not captured by the PCC. Among different kernel functions, the Polynomial function had the best performance. Applying this kernel, the classification was done by the Support Vector Machine (SVM) classifier. The achieved accuracy was equal to 98.68±0.79%. The Occipital and Temporal lobes and also the Default Mode Network (DMN) were analyzed and the kernel trick method showed more significant differences in all of them. It is worthwhile to mention that the right and left Angular areas of DMN showed no significant changes in none of the methods and it could be concluded that the AD does not affect this areas effectively.
Cognitive Biomedical Engineering
Zahra Soltanifar; Hamid Behnam; Anahita Khorrami Banaraki; Mojtaba Khodadadi; Behnoosh Hamed Ali; Ali Golbazi Mahdipour
Volume 15, Issue 3 , December 2021, , Pages 235-246
Abstract
The pattern of abnormal gaze is observed in individuals with autism spectrum disorders. Studies of eye movements in people with autism have shown significant difference in the pattern of staring at the eyes and mouth compared to control groups. Yet, findings have been contradictory to date, and in spite ...
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The pattern of abnormal gaze is observed in individuals with autism spectrum disorders. Studies of eye movements in people with autism have shown significant difference in the pattern of staring at the eyes and mouth compared to control groups. Yet, findings have been contradictory to date, and in spite of the fact that previous studies on eye dazzling in people with autism are expanding, the findings still do not appear to be consistent. Thus, we tracked eye movements in face processing for 25 teenagers with autism and 25 teenagers from the control group to examine any abnormal concentration in the facial areas. Experimental task used in this study includes standard images of the emotional states of the male and female faces (roundness of the face) in the state of anger, surprise, happiness, sadness and neutrality and subjects looked at these faces, while the eye tracker recorded their eye movements. In this task, they were required to select the displayed emotional state by the reply box. The selected Boosted Trees Ensemble classifier was able to use features related to the total data received from eye tracking in face segmentation into 8 areas (forehead, right and left eye, right and left cheek, nose, mouth and chin) with an accuracy of 83.31% in separating the two groups of autism and control. Moreover, in the study of facial components, left eye, left cheek, right cheek, and right eye, with 84.18%, 83.85%, 82.73% and 81.25% accuracy respectively, were able to make the most difference in the classification. Non-normal patterns in eye gaze can be very important because biomarkers indicate a condition that can be used for early diagnosis. It can also be a guide for researchers to design a game based on the results of this paper to improve the social interactions by strengthening eye contact for people with autism.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Hadi Grailu
Volume 15, Issue 3 , December 2021, , Pages 247-262
Abstract
Today, auscultation is one of the most effective methods in monitoring heart disease. With the advancement of technology and the facilitation of telecare on the one hand, and the increasing need for high quality and long-term recording of cardiac audio signals on the other hand, the amount of data generated ...
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Today, auscultation is one of the most effective methods in monitoring heart disease. With the advancement of technology and the facilitation of telecare on the one hand, and the increasing need for high quality and long-term recording of cardiac audio signals on the other hand, the amount of data generated has increased and therefore, the storage and transmission of these signals has become a challenge. This, in turn, demonstrates the importance and necessity of using efficient methods for compression of these types of signals. In this paper, a lossy compression method is proposed for PCG signals recorded at a relatively high sampling rate so that it can control the quality of the compressed signal. This method is based on two techniques: "two-stage downsampling" and "pattern matching". The proposed two-stage downsampling technique increases the amount of compression ratio and at the same time reduces the computational complexity. The pattern matching technique is able to reduce the inter-period redundancy and therefore, increase the compression ratio. The simulation results of the proposed method on the two databases of the University of Michigan and the University of Washington showed that the two-stage downsampling and pattern matching techniques have a large contribution in increasing the compression ratio. The performance of the proposed method was evaluated according to the PRD and CR criteria and compared with that of some existing methods. In this evaluation, for the PRD range of 5%, the CR value was between 2500 and 3900 for the University of Michigan database and between 2500 and 4125 for the University of Washington database. Also, the results of applying the proposed method on the Pascal database showed that the efficiency of the proposed method depends to a large extent on the quality and regularity of the input PCG signals.
Neural Engineering / Neuroengineering / Brain Engineering
Ghazaleh Soleimani; Mehrdad Saviz; Farzad Towhidkhah; Hamed Ekhtiari
Volume 14, Issue 3 , October 2020, , Pages 251-266
Abstract
Transcranial direct current stimulation (tDCS) is the most-used non-invasive brain stimulation method. However, the main challenge in tDCS studies is its heterogeneity and large inter-individual variability in response. Brain anatomy, that varies from person to person, can change electric field distribution ...
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Transcranial direct current stimulation (tDCS) is the most-used non-invasive brain stimulation method. However, the main challenge in tDCS studies is its heterogeneity and large inter-individual variability in response. Brain anatomy, that varies from person to person, can change electric field distribution patterns in the brain and should be considered as a source of variation. Previous findings support that tDCS-induced EFs affect brain activity and ultimately change behavioral outcomes. Nonetheless, the exact relationship between EFs and brain activity alterations has not yet been investigated. In this randomized double-blinded sham-controlled crossover study, 14 subjects with methamphetamine use disorders were recruited and tDCS with 2 mA current intensity was applied over the dorsolateral prefrontal cortex. Each subject participated in two sessions for sham or real stimulation with at least a 1-week washout period. In each session, structural and functional MRI during a cue-induced craving task were collected immediately before and after tDCS. Individualized computational head models were simulated based on structural MR images and finite element methods. Group-level analysis of the models showed inter-individual variability across the subjects with maximum electric field intensity in frontal pole (0.3424±0.07). Furthermore, functional data, based on a drug minus neutral contrast, showed that real versus sham stimulation decreased brain activity in superior temporal gyrus and posterior cingulate cortex (P<0.001). However, we did not find a significant correlation between induced EFs and brain activity alterations. In sum, in this study, we suggested a pipeline for integrating electric fields with functional neuroimaging data to bring new insights into the tDCS mechanism of action and future studies are required to establish, or to refute, this conclusion.
Dental Biomechanics
Pedram Akhlaghi; Setareh Khorshidparast; Gholamreza Rouhi
Volume 15, Issue 3 , December 2021, , Pages 263-277
Abstract
Today, the success and failure of treatment by dental implants is influenced by the concept of primary and secondary stability. Primary stability is the capacity of the bone-implant system to withstand the loads, without noticeable damage to the adjacent bone, which may cause the implant to loosen, and ...
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Today, the success and failure of treatment by dental implants is influenced by the concept of primary and secondary stability. Primary stability is the capacity of the bone-implant system to withstand the loads, without noticeable damage to the adjacent bone, which may cause the implant to loosen, and thus the implantation process fails. The aim of this study was to develop a micro-finite element (μFE) model and validate it with an in-vitro mechanical test, in order to evaluate the primary stability of dental implants by measuring the stiffness and ultimate load of the bone-implant system through cyclic compressive loading-unloading test. After bone-implant preparation, a quasi-static compressive step-wise loading-unloading cycles, with a displacement rate of 0.0024 mm/s and displacement-controlled were applied to the bone-implant structure with the amplitudes of 0.04 mm to 1.28 mm. Force-displacement curve and the stiffness of the structure in each step then were obtained. Prior to loading, the bony sample was scanned through a μCT device and a μFE model was developed based on the boundary and loading conditions similar to the in-vitro test to predict the force-displacement curve of the structure. Finally, the predicted force-displacement curve from μFE model was compared with the results of the experimental in-vitro test. Results showed that the predicted force-displacement curve from the μFE model is in agreement with the results of the experimental test. The μFE model developed here has the capability to show the overall response of the bone-implant structure under large deformations, and can also be used as a tool to improve the design of the dental implants, with the ultimate goal of increasing the stability of dental implants in immediate loading dental implants.
