Ali Khadem - Academia.edu (original) (raw)

Papers by Ali Khadem

2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)

Frontiers in Biomedical Technologies

Purpose: Sleep is one of the necessities of the body, such as eating, drinking, etc., that affect... more Purpose: Sleep is one of the necessities of the body, such as eating, drinking, etc., that affects different aspects of human life. Sleep monitoring and sleep stage classification play an important role in the diagnosis of sleeprelated diseases and neurological disorders. Empirically, classification of sleep stages is a time-consuming, tedious, and complex task, which heavily depends on the experience of the experts. As a result, there is a crucial need for an automatic efficient sleep staging system. Materials and Methods: This study develops a 13-layer 1D Convolutional Neural Network (CNN) using singlechannel Electroencephalogram (EEG) signal for extracting features automatically and classifying the sleep stages. To overcome the negative effect of an imbalance dataset, we have used the Synthetic Minority Oversampling Technique (SMOTE). In our study, the single-channel EEG signal is given to a 1D CNN, without any feature extraction/selection processes. This deep network can self-le...

Frontiers in Biomedical Technologies

Purpose: Attention Deficit Hyperactivity Disorder (ADHD) is now recognized as the most common chi... more Purpose: Attention Deficit Hyperactivity Disorder (ADHD) is now recognized as the most common childhood behavioral disorder. This disorder causes school problems and social incompatibility. Thus an accurate diagnosis can help diminish such problems. In this paper, we propose a brain connectomics approach based on eyes-open resting state Magnetoencephalography (rs-MEG) to diagnose subjects with ADHD from Healthy Controls (HC). Materials and Methods: We used the eyes-open rs-MEG signals recorded from 25 subjects with ADHD and 25 HC. We calculated Coherence (COH) between the MEG sensors in the conventional frequency bands (i.e., delta, theta, alpha, beta, and gamma), selected the most discriminative COH measures by the Neighborhood Component Analysis (NCA), and fed them to three classifiers, including Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel, K-Nearest Neighbors (KNN), and Decision Tree to classify ADHD and HC. Results: We achieved the best average accuracy ...

2021 11th International Conference on Computer Engineering and Knowledge (ICCKE)

Accurate diagnosis of Autism Spectrum Disorder (ASD) is essential for its management and rehabili... more Accurate diagnosis of Autism Spectrum Disorder (ASD) is essential for its management and rehabilitation. Neuroimaging techniques that are non-invasive are disease markers and may be leveraged to aid ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, diagnosing ASD with neuroimaging data without exploiting artificial intelligence (AI) techniques is extremely challenging. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. In this paper, studies conducted with the aid of DL networks to distinguish ASD wer...

2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME)

Brain-computer interface (BCI) is defined as the combination of machine and brain signals to cont... more Brain-computer interface (BCI) is defined as the combination of machine and brain signals to control a device or computer to improve the quality of life, e.g., for people with paralysis. In this paper, we focus on people with speech disorders and investigate the capability of electroencephalogram (EEG) signals to discriminate four classes, including the speech imagination of three Persian words corresponding to the English words "rock," "paper," and "scissors," in addition to the resting state. We used the data available from the 3rd Iranian BCI competition (iBCIC2020), acquired from 10 healthy participants in a randomized study. Initially, the mutual information (MI) was used to find the optimum frequency band. Then, features were extracted from the data using the Common Spatial Pattern (CSP) algorithm. Afterward, the most discriminative features were selected using the neighborhood component analysis (NCA). These features were then fed to a meta-classifier based on the stacking ensemble learning. The results show that working on an optimum frequency band will enhance the results compared with the fixed frequency band. It is also worth mentioning that the optimum frequency band is subject dependent; therefore, it is substantial to be selected accurately. Our method achieved an average classification accuracy of 51.90%±2.73 across all participants, which is promising compared with the results of previous studies in the field of imagined speech recognition in subject dependent BCI systems with randomized order of the stimuli.

2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME)

In recent years, increasing attention has been paid to the study of brain connectivity in order t... more In recent years, increasing attention has been paid to the study of brain connectivity in order to detect brain abnormalities and to raise awareness of brain disorders like Autism spectrum disorder (ASD). In these studies, the brain connectivity network is estimated and its graph parameters are extracted to aid researchers in analyzing brain function and its disorders during various tasks. Selecting the suitable effective connectivity estimator which is able to estimate linear and nonlinear causal relationships is an important issue in accurate estimation of effective connectivity network and exploring its disorders. In this paper, we address this issue and also investigate the effect of choosing the effective connectivity estimator on detected abnormalities of effective connectivity graph of ASD subjects. Two well-known effective connectivity estimators are used: transfer entropy (TE) and granger causality index (GCI). We first simulate three different networks whose their causal connections have different linearity conditions and compare the sensitivity and specificity of TE and GCI in each case. It is shown that except in completely linear networks, TE generally outperforms GCI in terms of both sensitivity and specificity. In the next step, each of TE and GCI is applied to an EEG dataset recorded during a face processing task from two groups of healthy control (He) individuals and people with ASD. The networks estimated from the subjects of two groups are compared in terms of average degree, average path length and total clustering coefficient. It can be seen that just the average degree is significantly different (higher) in healthy subjects than in ASD patients by using both TE and GCI. So the results of both TE and GCI are in accordance with the underconnectivity theory of ASD.

This article presents a hybrid boundary element-finite element (BE–FE) method to solve the EEG fo... more This article presents a hybrid boundary element-finite element (BE–FE) method to solve the EEG forward problem and take advantages of both the boundary element method (BEM) and finite element method (FEM). Although realistic EEG forward problems with heterogeneous and anisotropic regions can be solved by FEM accurately, the FEM modeling of the brain with dipolar sources may lead to singularity. In contrast, the BEM can solve EEG forward problems with isotropic tissue regions and dipolar sources using a suitable integral formulation. This work utilizes both FEM and BEM strengths attained by dividing the regions into some homogeneous BE regions with sources and some heterogeneous and anisotropic FE regions. Furthermore, the BEM is applied for modeling the brain, including dipole sources and the FEM for other head layers. To validate the proposed method, inhomogeneous isotropic/anisotropic three– and four–layer spherical head models are studied. Moreover, a four&-layer realistic head m...

Joint structural-functional (S-F) developmental studies present a novel approach to address the c... more Joint structural-functional (S-F) developmental studies present a novel approach to address the complex neuroscience questions on how the human brain works and how it matures. Joint S-F biomarkers have the inherent potential to model effectively the brain’s maturation, fill the information gap in temporal brain atlases, and demonstrate how the brain’s performance matures during the lifespan. This review presents the current state of knowledge on heterochronous and heterogeneous development of S-F links during the maturation period. The S-F relationship has been investigated in early-matured unimodal and prolonged-matured transmodal regions of the brain using a variety of structural and functional biomarkers and data acquisition modalities. Joint S-F unimodal studies have employed auditory and visual stimuli, while the main focus of joint S-F transmodal studies has been resting-state networks and working memory. However, non-significant associations between some structural and functi...

2020 28th Iranian Conference on Electrical Engineering (ICEE)

Neurons are functionally classified into inhibitory and excitatory categories based on the influe... more Neurons are functionally classified into inhibitory and excitatory categories based on the influence they have on the firing rates of their postsynaptic neurons after being stimulated. Although assessing the firing rates of postsynaptic neurons is the main way of this categorization, it is very hard in real cases. Due to the lack of a labelled dataset with inhibitory and excitatory neurons, past studies have been conducted to investigate the feasibility of this categorization based on clustering some features of the spike waveforms and evaluating the results by physiological evidence. However, there is still the lack of a classification study in order to do this categorization by using features of spike waveforms and different classifiers. This is what we addressed in this paper based on a recent labeled dataset of mouse hippocampus neurons. We extracted nine different features from neuron spikes. Then we investigated the significance of difference of each feature between inhibitory and excitatory groups using Wilcoxon rank-sum-test and also evaluated the effectiveness of all possible feature subsets for classification using KNN, LDA, and SVM classifiers. The highest average classification accuracy was %96.96 obtained by using SVM with RBF kernel and five features. However, KNN yielded %96.08 average accuracy by using just one feature which was Peak amplitude asymmetry. In addition, Peak amplitude asymmetry, Peak-to-trough ratio, and Duration between peaks selected more in the optimum feature subsets using different classifiers. Generally, we concluded the features obtained from waveform spikes and simple common classifiers can effectively classify neurons into inhibitory and excitatory categories.

Purpose: Sleep is one of the necessities of the body, such as eating, drinking, etc., that affect... more Purpose: Sleep is one of the necessities of the body, such as eating, drinking, etc., that affects different aspects of human life. Sleep monitoring and sleep stage classification play an important role in the diagnosis of sleeprelated diseases and neurological disorders. Empirically, classification of sleep stages is a time-consuming, tedious, and complex task, which heavily depends on the experience of the experts. As a result, there is a crucial need for an automatic efficient sleep staging system. Materials and Methods: This study develops a 13-layer 1D Convolutional Neural Network (CNN) using singlechannel Electroencephalogram (EEG) signal for extracting features automatically and classifying the sleep stages. To overcome the negative effect of an imbalance dataset, we have used the Synthetic Minority Oversampling Technique (SMOTE). In our study, the single-channel EEG signal is given to a 1D CNN, without any feature extraction/selection processes. This deep network can self-le...

2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), 2016

Ultrasound elastography is a noninvasive technique for mapping the elasticity of soft tissues as ... more Ultrasound elastography is a noninvasive technique for mapping the elasticity of soft tissues as an image called elastogram (axial-strain image). Applying a small axial pressure on the tissue surface is the main requirement of ultrasound elastography. If this pressure is applied manually by the ultrasound probe the technique is called “freehand ultrasound elastography”. Non-ideal manual compressions lead to emergence of undesired artifacts in the elastograms which degrade their quality and restrict their clinical applicability. In this paper we propose a method based on Multiscale Principal Component Analysis (MSPCA) to suppress the elastographic artifacts and yield refined elastograms with better Elastographic Signal to Noise Ratio (SNRe) and Elastographic Contrast to Noise Ratio (CNRe). We applied our proposed method to a freehand elastographic dataset of a phantom which mimicked a hard tumor in a soft background tissue. The results showed significant improvements in average SNRe ...

ArXiv, 2020

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people ar... more Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL te...

Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the... more Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent Deep Learning (DL)based methods for automated SZ diagnosis via EEG signals. The obtained results are compared with those of conventional intelligent methods. In order to implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals are divided into 25-seconds time frames and then were normalized by zscore or norm L2. In the classification step, two different approaches are considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals is first carried out by conventional DL methods, e.g., KNN, DT, SVM, Bayes, bagging, RF, and ET. Various proposed DL models, including LSTMs, 1D-CNNs, and 1D-CNN-LSTMs, are...

ArXiv, 2021

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adult... more Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed ∗Corresponding author Email address: afshin.shoeibi@gmail.com (Afshin Shoeibi) 1Equal contribution Preprint submitted to Elsevier March 5, 2021 ar X iv :2 10 3. 03 08 1v 1 [ cs .L G ] 2 4 Fe b 20 21 with advanc...

ArXiv, 2021

Epileptic seizures are a type of neurological disorder that affect many people worldwide. Special... more Epileptic seizures are a type of neurological disorder that affect many people worldwide. Specialist physicians and neurologists take advantage of structural and functional neuroimaging modalities to diagnose various types of epileptic seizures. Neuroimaging modalities assist specialist physicians considerably in analyzing brain tissue and the changes made in it. One method to accelerate the accurate and fast diagnosis of epileptic seizures is to employ computer aided diagnosis systems (CADS) based on artificial intelligence (AI) and functional ∗Corresponding author Email addresses: afshin.shoeibi@gmail.com (Afshin Shoeibi ), navidghassemi@mail.um.ac.ir (Navid Ghassemi), khodatars1marjane@gmail.com (Marjane Khodatars), mahbube.jafari@yahoo.com (Mahboobeh Jafari), parisamoridian@yahoo.com (Parisa Moridian), ralizadehsani@deakin.edu.au (Roohallah Alizadehsani), alikhadem@kntu.ac.ir (Ali Khadem), yinan.kong@mq.edu.au (Yinan Kong), assefzare@gmail.com (Assef Zare), gorriz@ugr.es (Juan M...

2017 Iranian Conference on Electrical Engineering (ICEE)

Many people worldwide suffer from Autism Spectrum Disorder (ASD) which is a neurodevelopmental di... more Many people worldwide suffer from Autism Spectrum Disorder (ASD) which is a neurodevelopmental disorder. It severely degrades the subjects' communication skills. The earlier diagnosing of ASD, The higher probability to prevent the severity of ASD symptoms. In the recent decade, brain connectivity studies on ASD subjects have converged to the theory of under-connectivity as a biomarker of ASD. Most of these studies have used fMRI data rather than EEG/MEG data and investigated functional connectivity rather than effective connectivity. There are few EEG/MEG studies which investigated the effective connectivity disorders in ASD subjects. Also, to the best of our knowledge there is no published study to investigate the disorders of brain effective connectivity networks in ASD subjects using EEG data, nonlinear effective connectivity measures and graph theory. In this paper, we aim to start filling this gap. We used EEG data, transfer entropy with self-prediction optimality, and four graph theoretic parameters to compare the effective connectivity networks of ASD youths with those of healthy controls (HCs) during a passive face processing task. Our results showed a significant difference in average degree (p<0.05) between ASD and HC groups which is consistent with the under-connectivity theory of ASD. On the other hand we detected no significant changes in total clustering coefficient, average path length, and longest path length.

Computers in Biology and Medicine

2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)

The common process of window-based ultrasound elastography (USE) is based on estimating the displ... more The common process of window-based ultrasound elastography (USE) is based on estimating the displacement fields and calculating the strain images by differentiating them. The displacement fields can be obtained using operators such as normalized cross-correlation (NCC). However, the provided displacements suffer from the artifacts caused by two dominant sources, the decorrelation, and amplitude modulation (AM) error. The main contribution of this study is to obtain strain images of higher quality and reduce the effect of artifacts. To this end, a weighted displacement estimation method is proposed. The proposed method consists of two steps. Some changes are applied to the windowing process, speeding up displacement estimation using the NCC method and filtering the provided displacements in two stages using median and weighted windows. We have assessed the performance of different weighted windows in conjunction with NCC on simulation and phantom data. This evaluation has not been implemented on the displacement fields yet. Thus, we tried to represent their improving effects on the estimated strains compared to the simple NCC approach and suggest the best window. The quantitative evaluation is performed in terms of elastographic signal to noise ratio (SNRe) and elastographic contrast to noise ratio (CNRe) on simulation and phantom data. The root mean square error (RMSE) comparison is also implemented for the simulation data. The visual and quantitative comparisons reveal that the proposed method substantially reduces artifacts, improves the strain image quality, and outperforms the NCC method.

2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)

The well-timed and correct diagnosis of Bipolar Disorder (BD), followed by proper treatment, is v... more The well-timed and correct diagnosis of Bipolar Disorder (BD), followed by proper treatment, is vital for avoiding its progress. Resting-state functional magnetic resonance imaging (rs-fMRI) may have an essential role in diagnosing BD. In this research, a new approach is proposed to diagnose Bipolar I Disorder (BDI) from healthy controls (HCs). The proposed method uses a one-dimensional convolutional network (1D-CNN) as a feature extraction step on raw rs-fMRI time series data. The extracted features are then passed on to a multilayer perceptron in order to classify BDI subjects from HCs. 40 BDI subjects and 40 HCs were used in this study. 25% of the dataset was considered as test data, in which an accuracy of 70% was obtained with the final trained model. To the best of our knowledge no study before has investigated the use of 1D-CNN on raw rs-fMRI time series data for BDI diagnosis.

2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)

Frontiers in Biomedical Technologies

Purpose: Sleep is one of the necessities of the body, such as eating, drinking, etc., that affect... more Purpose: Sleep is one of the necessities of the body, such as eating, drinking, etc., that affects different aspects of human life. Sleep monitoring and sleep stage classification play an important role in the diagnosis of sleeprelated diseases and neurological disorders. Empirically, classification of sleep stages is a time-consuming, tedious, and complex task, which heavily depends on the experience of the experts. As a result, there is a crucial need for an automatic efficient sleep staging system. Materials and Methods: This study develops a 13-layer 1D Convolutional Neural Network (CNN) using singlechannel Electroencephalogram (EEG) signal for extracting features automatically and classifying the sleep stages. To overcome the negative effect of an imbalance dataset, we have used the Synthetic Minority Oversampling Technique (SMOTE). In our study, the single-channel EEG signal is given to a 1D CNN, without any feature extraction/selection processes. This deep network can self-le...

Frontiers in Biomedical Technologies

Purpose: Attention Deficit Hyperactivity Disorder (ADHD) is now recognized as the most common chi... more Purpose: Attention Deficit Hyperactivity Disorder (ADHD) is now recognized as the most common childhood behavioral disorder. This disorder causes school problems and social incompatibility. Thus an accurate diagnosis can help diminish such problems. In this paper, we propose a brain connectomics approach based on eyes-open resting state Magnetoencephalography (rs-MEG) to diagnose subjects with ADHD from Healthy Controls (HC). Materials and Methods: We used the eyes-open rs-MEG signals recorded from 25 subjects with ADHD and 25 HC. We calculated Coherence (COH) between the MEG sensors in the conventional frequency bands (i.e., delta, theta, alpha, beta, and gamma), selected the most discriminative COH measures by the Neighborhood Component Analysis (NCA), and fed them to three classifiers, including Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel, K-Nearest Neighbors (KNN), and Decision Tree to classify ADHD and HC. Results: We achieved the best average accuracy ...

2021 11th International Conference on Computer Engineering and Knowledge (ICCKE)

Accurate diagnosis of Autism Spectrum Disorder (ASD) is essential for its management and rehabili... more Accurate diagnosis of Autism Spectrum Disorder (ASD) is essential for its management and rehabilitation. Neuroimaging techniques that are non-invasive are disease markers and may be leveraged to aid ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, diagnosing ASD with neuroimaging data without exploiting artificial intelligence (AI) techniques is extremely challenging. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. In this paper, studies conducted with the aid of DL networks to distinguish ASD wer...

2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME)

Brain-computer interface (BCI) is defined as the combination of machine and brain signals to cont... more Brain-computer interface (BCI) is defined as the combination of machine and brain signals to control a device or computer to improve the quality of life, e.g., for people with paralysis. In this paper, we focus on people with speech disorders and investigate the capability of electroencephalogram (EEG) signals to discriminate four classes, including the speech imagination of three Persian words corresponding to the English words "rock," "paper," and "scissors," in addition to the resting state. We used the data available from the 3rd Iranian BCI competition (iBCIC2020), acquired from 10 healthy participants in a randomized study. Initially, the mutual information (MI) was used to find the optimum frequency band. Then, features were extracted from the data using the Common Spatial Pattern (CSP) algorithm. Afterward, the most discriminative features were selected using the neighborhood component analysis (NCA). These features were then fed to a meta-classifier based on the stacking ensemble learning. The results show that working on an optimum frequency band will enhance the results compared with the fixed frequency band. It is also worth mentioning that the optimum frequency band is subject dependent; therefore, it is substantial to be selected accurately. Our method achieved an average classification accuracy of 51.90%±2.73 across all participants, which is promising compared with the results of previous studies in the field of imagined speech recognition in subject dependent BCI systems with randomized order of the stimuli.

2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME)

In recent years, increasing attention has been paid to the study of brain connectivity in order t... more In recent years, increasing attention has been paid to the study of brain connectivity in order to detect brain abnormalities and to raise awareness of brain disorders like Autism spectrum disorder (ASD). In these studies, the brain connectivity network is estimated and its graph parameters are extracted to aid researchers in analyzing brain function and its disorders during various tasks. Selecting the suitable effective connectivity estimator which is able to estimate linear and nonlinear causal relationships is an important issue in accurate estimation of effective connectivity network and exploring its disorders. In this paper, we address this issue and also investigate the effect of choosing the effective connectivity estimator on detected abnormalities of effective connectivity graph of ASD subjects. Two well-known effective connectivity estimators are used: transfer entropy (TE) and granger causality index (GCI). We first simulate three different networks whose their causal connections have different linearity conditions and compare the sensitivity and specificity of TE and GCI in each case. It is shown that except in completely linear networks, TE generally outperforms GCI in terms of both sensitivity and specificity. In the next step, each of TE and GCI is applied to an EEG dataset recorded during a face processing task from two groups of healthy control (He) individuals and people with ASD. The networks estimated from the subjects of two groups are compared in terms of average degree, average path length and total clustering coefficient. It can be seen that just the average degree is significantly different (higher) in healthy subjects than in ASD patients by using both TE and GCI. So the results of both TE and GCI are in accordance with the underconnectivity theory of ASD.

This article presents a hybrid boundary element-finite element (BE–FE) method to solve the EEG fo... more This article presents a hybrid boundary element-finite element (BE–FE) method to solve the EEG forward problem and take advantages of both the boundary element method (BEM) and finite element method (FEM). Although realistic EEG forward problems with heterogeneous and anisotropic regions can be solved by FEM accurately, the FEM modeling of the brain with dipolar sources may lead to singularity. In contrast, the BEM can solve EEG forward problems with isotropic tissue regions and dipolar sources using a suitable integral formulation. This work utilizes both FEM and BEM strengths attained by dividing the regions into some homogeneous BE regions with sources and some heterogeneous and anisotropic FE regions. Furthermore, the BEM is applied for modeling the brain, including dipole sources and the FEM for other head layers. To validate the proposed method, inhomogeneous isotropic/anisotropic three– and four–layer spherical head models are studied. Moreover, a four&-layer realistic head m...

Joint structural-functional (S-F) developmental studies present a novel approach to address the c... more Joint structural-functional (S-F) developmental studies present a novel approach to address the complex neuroscience questions on how the human brain works and how it matures. Joint S-F biomarkers have the inherent potential to model effectively the brain’s maturation, fill the information gap in temporal brain atlases, and demonstrate how the brain’s performance matures during the lifespan. This review presents the current state of knowledge on heterochronous and heterogeneous development of S-F links during the maturation period. The S-F relationship has been investigated in early-matured unimodal and prolonged-matured transmodal regions of the brain using a variety of structural and functional biomarkers and data acquisition modalities. Joint S-F unimodal studies have employed auditory and visual stimuli, while the main focus of joint S-F transmodal studies has been resting-state networks and working memory. However, non-significant associations between some structural and functi...

2020 28th Iranian Conference on Electrical Engineering (ICEE)

Neurons are functionally classified into inhibitory and excitatory categories based on the influe... more Neurons are functionally classified into inhibitory and excitatory categories based on the influence they have on the firing rates of their postsynaptic neurons after being stimulated. Although assessing the firing rates of postsynaptic neurons is the main way of this categorization, it is very hard in real cases. Due to the lack of a labelled dataset with inhibitory and excitatory neurons, past studies have been conducted to investigate the feasibility of this categorization based on clustering some features of the spike waveforms and evaluating the results by physiological evidence. However, there is still the lack of a classification study in order to do this categorization by using features of spike waveforms and different classifiers. This is what we addressed in this paper based on a recent labeled dataset of mouse hippocampus neurons. We extracted nine different features from neuron spikes. Then we investigated the significance of difference of each feature between inhibitory and excitatory groups using Wilcoxon rank-sum-test and also evaluated the effectiveness of all possible feature subsets for classification using KNN, LDA, and SVM classifiers. The highest average classification accuracy was %96.96 obtained by using SVM with RBF kernel and five features. However, KNN yielded %96.08 average accuracy by using just one feature which was Peak amplitude asymmetry. In addition, Peak amplitude asymmetry, Peak-to-trough ratio, and Duration between peaks selected more in the optimum feature subsets using different classifiers. Generally, we concluded the features obtained from waveform spikes and simple common classifiers can effectively classify neurons into inhibitory and excitatory categories.

Purpose: Sleep is one of the necessities of the body, such as eating, drinking, etc., that affect... more Purpose: Sleep is one of the necessities of the body, such as eating, drinking, etc., that affects different aspects of human life. Sleep monitoring and sleep stage classification play an important role in the diagnosis of sleeprelated diseases and neurological disorders. Empirically, classification of sleep stages is a time-consuming, tedious, and complex task, which heavily depends on the experience of the experts. As a result, there is a crucial need for an automatic efficient sleep staging system. Materials and Methods: This study develops a 13-layer 1D Convolutional Neural Network (CNN) using singlechannel Electroencephalogram (EEG) signal for extracting features automatically and classifying the sleep stages. To overcome the negative effect of an imbalance dataset, we have used the Synthetic Minority Oversampling Technique (SMOTE). In our study, the single-channel EEG signal is given to a 1D CNN, without any feature extraction/selection processes. This deep network can self-le...

2016 23rd Iranian Conference on Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), 2016

Ultrasound elastography is a noninvasive technique for mapping the elasticity of soft tissues as ... more Ultrasound elastography is a noninvasive technique for mapping the elasticity of soft tissues as an image called elastogram (axial-strain image). Applying a small axial pressure on the tissue surface is the main requirement of ultrasound elastography. If this pressure is applied manually by the ultrasound probe the technique is called “freehand ultrasound elastography”. Non-ideal manual compressions lead to emergence of undesired artifacts in the elastograms which degrade their quality and restrict their clinical applicability. In this paper we propose a method based on Multiscale Principal Component Analysis (MSPCA) to suppress the elastographic artifacts and yield refined elastograms with better Elastographic Signal to Noise Ratio (SNRe) and Elastographic Contrast to Noise Ratio (CNRe). We applied our proposed method to a freehand elastographic dataset of a phantom which mimicked a hard tumor in a soft background tissue. The results showed significant improvements in average SNRe ...

ArXiv, 2020

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people ar... more Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA. This virus has one of the largest RNA genomes and is approximately 120 nm. The X-Ray and computed tomography (CT) imaging modalities are widely used to obtain a fast and accurate medical diagnosis. Identifying COVID-19 from these medical images is extremely challenging as it is time-consuming, demanding, and prone to human errors. Hence, artificial intelligence (AI) methodologies can be used to obtain consistent high performance. Among the AI methodologies, deep learning (DL) networks have gained much popularity compared to traditional machine learning (ML) methods. Unlike ML techniques, all stages of feature extraction, feature selection, and classification are accomplished automatically in DL models. In this paper, a complete survey of studies on the application of DL te...

Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the... more Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent Deep Learning (DL)based methods for automated SZ diagnosis via EEG signals. The obtained results are compared with those of conventional intelligent methods. In order to implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals are divided into 25-seconds time frames and then were normalized by zscore or norm L2. In the classification step, two different approaches are considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals is first carried out by conventional DL methods, e.g., KNN, DT, SVM, Bayes, bagging, RF, and ET. Various proposed DL models, including LSTMs, 1D-CNNs, and 1D-CNN-LSTMs, are...

ArXiv, 2021

Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adult... more Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed the temporal and anterior lobes of hippocampus regions of brain get affected by SZ. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. The magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed ∗Corresponding author Email address: afshin.shoeibi@gmail.com (Afshin Shoeibi) 1Equal contribution Preprint submitted to Elsevier March 5, 2021 ar X iv :2 10 3. 03 08 1v 1 [ cs .L G ] 2 4 Fe b 20 21 with advanc...

ArXiv, 2021

Epileptic seizures are a type of neurological disorder that affect many people worldwide. Special... more Epileptic seizures are a type of neurological disorder that affect many people worldwide. Specialist physicians and neurologists take advantage of structural and functional neuroimaging modalities to diagnose various types of epileptic seizures. Neuroimaging modalities assist specialist physicians considerably in analyzing brain tissue and the changes made in it. One method to accelerate the accurate and fast diagnosis of epileptic seizures is to employ computer aided diagnosis systems (CADS) based on artificial intelligence (AI) and functional ∗Corresponding author Email addresses: afshin.shoeibi@gmail.com (Afshin Shoeibi ), navidghassemi@mail.um.ac.ir (Navid Ghassemi), khodatars1marjane@gmail.com (Marjane Khodatars), mahbube.jafari@yahoo.com (Mahboobeh Jafari), parisamoridian@yahoo.com (Parisa Moridian), ralizadehsani@deakin.edu.au (Roohallah Alizadehsani), alikhadem@kntu.ac.ir (Ali Khadem), yinan.kong@mq.edu.au (Yinan Kong), assefzare@gmail.com (Assef Zare), gorriz@ugr.es (Juan M...

2017 Iranian Conference on Electrical Engineering (ICEE)

Many people worldwide suffer from Autism Spectrum Disorder (ASD) which is a neurodevelopmental di... more Many people worldwide suffer from Autism Spectrum Disorder (ASD) which is a neurodevelopmental disorder. It severely degrades the subjects' communication skills. The earlier diagnosing of ASD, The higher probability to prevent the severity of ASD symptoms. In the recent decade, brain connectivity studies on ASD subjects have converged to the theory of under-connectivity as a biomarker of ASD. Most of these studies have used fMRI data rather than EEG/MEG data and investigated functional connectivity rather than effective connectivity. There are few EEG/MEG studies which investigated the effective connectivity disorders in ASD subjects. Also, to the best of our knowledge there is no published study to investigate the disorders of brain effective connectivity networks in ASD subjects using EEG data, nonlinear effective connectivity measures and graph theory. In this paper, we aim to start filling this gap. We used EEG data, transfer entropy with self-prediction optimality, and four graph theoretic parameters to compare the effective connectivity networks of ASD youths with those of healthy controls (HCs) during a passive face processing task. Our results showed a significant difference in average degree (p<0.05) between ASD and HC groups which is consistent with the under-connectivity theory of ASD. On the other hand we detected no significant changes in total clustering coefficient, average path length, and longest path length.

Computers in Biology and Medicine

2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)

The common process of window-based ultrasound elastography (USE) is based on estimating the displ... more The common process of window-based ultrasound elastography (USE) is based on estimating the displacement fields and calculating the strain images by differentiating them. The displacement fields can be obtained using operators such as normalized cross-correlation (NCC). However, the provided displacements suffer from the artifacts caused by two dominant sources, the decorrelation, and amplitude modulation (AM) error. The main contribution of this study is to obtain strain images of higher quality and reduce the effect of artifacts. To this end, a weighted displacement estimation method is proposed. The proposed method consists of two steps. Some changes are applied to the windowing process, speeding up displacement estimation using the NCC method and filtering the provided displacements in two stages using median and weighted windows. We have assessed the performance of different weighted windows in conjunction with NCC on simulation and phantom data. This evaluation has not been implemented on the displacement fields yet. Thus, we tried to represent their improving effects on the estimated strains compared to the simple NCC approach and suggest the best window. The quantitative evaluation is performed in terms of elastographic signal to noise ratio (SNRe) and elastographic contrast to noise ratio (CNRe) on simulation and phantom data. The root mean square error (RMSE) comparison is also implemented for the simulation data. The visual and quantitative comparisons reveal that the proposed method substantially reduces artifacts, improves the strain image quality, and outperforms the NCC method.

2021 5th International Conference on Pattern Recognition and Image Analysis (IPRIA)

The well-timed and correct diagnosis of Bipolar Disorder (BD), followed by proper treatment, is v... more The well-timed and correct diagnosis of Bipolar Disorder (BD), followed by proper treatment, is vital for avoiding its progress. Resting-state functional magnetic resonance imaging (rs-fMRI) may have an essential role in diagnosing BD. In this research, a new approach is proposed to diagnose Bipolar I Disorder (BDI) from healthy controls (HCs). The proposed method uses a one-dimensional convolutional network (1D-CNN) as a feature extraction step on raw rs-fMRI time series data. The extracted features are then passed on to a multilayer perceptron in order to classify BDI subjects from HCs. 40 BDI subjects and 40 HCs were used in this study. 25% of the dataset was considered as test data, in which an accuracy of 70% was obtained with the final trained model. To the best of our knowledge no study before has investigated the use of 1D-CNN on raw rs-fMRI time series data for BDI diagnosis.