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27 results on '"Parisa Moridian"'

1. Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

Author

Parisa Moridian, Navid Ghassemi, Mahboobeh Jafari, Salam Salloum-Asfar, Delaram Sadeghi, Marjane Khodatars, Afshin Shoeibi, Abbas Khosravi, Sai Ho Ling, Abdulhamit Subasi, Roohallah Alizadehsani, Juan M. Gorriz, Sara A. Abdulla, and U. Rajendra Acharya

Subjects
ASD diagnosis, neuroimaging, MRI modalities, machine learning, deep learning, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.

Published
2022
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2. Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models

Author

Afshin Shoeibi, Delaram Sadeghi, Parisa Moridian, Navid Ghassemi, Jónathan Heras, Roohallah Alizadehsani, Ali Khadem, Yinan Kong, Saeid Nahavandi, Yu-Dong Zhang, and Juan Manuel Gorriz

Subjects
schizophrenia, neuroimaging, EEG signals, diagnosis, deep learning, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

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 electroencephalography (EEG) signals. The obtained results are compared with those of conventional intelligent methods. To implement the proposed methods, the dataset of the Institute of Psychiatry and Neurology in Warsaw, Poland, has been used. First, EEG signals were divided into 25 s time frames and then were normalized by z-score or norm L2. In the classification step, two different approaches were considered for SZ diagnosis via EEG signals. In this step, the classification of EEG signals was first carried out by conventional machine learning methods, e.g., support vector machine, k-nearest neighbors, decision tree, naïve Bayes, random forest, extremely randomized trees, and bagging. Various proposed DL models, namely, long short-term memories (LSTMs), one-dimensional convolutional networks (1D-CNNs), and 1D-CNN-LSTMs, were used in the following. In this step, the DL models were implemented and compared with different activation functions. Among the proposed DL models, the CNN-LSTM architecture has had the best performance. In this architecture, the ReLU activation function with the z-score and L2-combined normalization was used. The proposed CNN-LSTM model has achieved an accuracy percentage of 99.25%, better than the results of most former studies in this field. It is worth mentioning that to perform all simulations, the k-fold cross-validation method with k = 5 has been used.

Published
2021
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3. Design of Adaptive-Robust Controller for Multi-State Synchronization of Chaotic Systems with Unknown and Time-Varying Delays and Its Application in Secure Communication

Author

Ali Akbar Kekha Javan, Afshin Shoeibi, Assef Zare, Navid Hosseini Izadi, Mahboobeh Jafari, Roohallah Alizadehsani, Parisa Moridian, Amir Mosavi, U. Rajendra Acharya, and Saeid Nahavandi

Subjects
time-delayed chaotic systems, adaptive-robust control, multi-state synchronization, unknown time delays, secure communications, time varying parameter, Chemical technology, TP1-1185
Abstract

In this paper, the multi-state synchronization of chaotic systems with non-identical, unknown, and time-varying delay in the presence of external perturbations and parametric uncertainties was studied. The presence of unknown delays, unknown bounds of disturbance and uncertainty, as well as changes in system parameters complicate the determination of control function and synchronization. During a synchronization scheme using a robust-adaptive control procedure with the help of the Lyapunov stability theorem, the errors converged to zero, and the updating rules were set to estimate the system parameters and delays. To investigate the performance of the proposed design, simulations have been carried out on two Chen hyper-chaotic systems as the slave and one Chua hyper-chaotic system as the master. Our results showed that the proposed controller outperformed the state-of-the-art techniques in terms of convergence speed of synchronization, parameter estimation, and delay estimation processes. The parameters and time delays were achieved with appropriate approximation. Finally, secure communication was realized with a chaotic masking method, and our results revealed the effectiveness of the proposed method in secure telecommunications.

Published
2021
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21. Epileptic seizure detection using deep learning techniques: A Review.

Author

Afshin Shoeibi, Navid Ghassemi, Marjane Khodatars, Mahboobeh Jafari, Sadiq Hussain, Roohallah Alizadehsani, Parisa Moridian, Abbas Khosravi, Hossein Hosseini-Nejad, Modjtaba Rouhani, Assef Zare, Ali Khadem, Saeid Nahavandi, Amir F. Atiya, and U. Rajendra Acharya

Published
2020

25. Epileptic Seizures Detection Using Deep Learning Techniques: A Review

Author

Navid Ghassemi, Hossein Hosseini-Nejad, Marjane Khodatars, Saeid Nahavandi, Maryam Panahiazar, Assef Zare, Udyavara Rajendra Acharya, Sadiq Hussain, Amir F. Atiya, Diba Aminshahidi, Mahboobeh Jafari, Fahime Khozeimeh, Modjtaba Rouhani, Parisa Moridian, Afshin Shoeibi, Abbas Khosravi, and Roohallah Alizadehsani

Subjects
Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, diagnosis, Health, Toxicology and Mutagenesis, Feature extraction, epileptic seizures, Machine Learning (stat.ML), Review, 02 engineering and technology, Electroencephalography, Machine learning, computer.software_genre, Machine Learning (cs.LG), 03 medical and health sciences, 0302 clinical medicine, Neuroimaging, Statistics - Machine Learning, Artificial Intelligence, Seizures, FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, medicine, Humans, EEG, Electrical Engineering and Systems Science - Signal Processing, Epilepsy, Modalities, medicine.diagnostic_test, business.industry, Deep learning, feature extraction, Public Health, Environmental and Occupational Health, deep learning, ComputingMethodologies_PATTERNRECOGNITION, classification, Medicine, 020201 artificial intelligence & image processing, Artificial intelligence, Epileptic seizure, medicine.symptom, business, computer, Algorithms, 030217 neurology & neurosurgery, MRI
Abstract

A variety of screening approaches have been proposed to diagnose epileptic seizures, using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities. Artificial intelligence encompasses a variety of areas, and one of its branches is deep learning (DL). Before the rise of DL, conventional machine learning algorithms involving feature extraction were performed. This limited their performance to the ability of those handcrafting the features. However, in DL, the extraction of features and classification are entirely automated. The advent of these techniques in many areas of medicine, such as in the diagnosis of epileptic seizures, has made significant advances. In this study, a comprehensive overview of works focused on automated epileptic seizure detection using DL techniques and neuroimaging modalities is presented. Various methods proposed to diagnose epileptic seizures automatically using EEG and MRI modalities are described. In addition, rehabilitation systems developed for epileptic seizures using DL have been analyzed, and a summary is provided. The rehabilitation tools include cloud computing techniques and hardware required for implementation of DL algorithms. The important challenges in accurate detection of automated epileptic seizures using DL with EEG and MRI modalities are discussed. The advantages and limitations in employing DL-based techniques for epileptic seizures diagnosis are presented. Finally, the most promising DL models proposed and possible future works on automated epileptic seizure detection are delineated.

Published
2021

26. Design of Adaptive-Robust Controller for Multi-State Synchronization of Chaotic Systems with Unknown and Time-Varying Delays and Its Application in Secure Communication

Author

Afshin Shoeibi, Mahboobeh Jafari, Saeid Nahavandi, Amirhosein Mosavi, Assef Zare, Navid Hosseini Izadi, Roohallah Alizadehsani, Ali Akbar Kekha Javan, Parisa Moridian, and U. Rajendra Acharya

Subjects
0209 industrial biotechnology, Computer science, unknown time delays, time varying parameter, 02 engineering and technology, lcsh:Chemical technology, Biochemistry, Article, Synchronization, secure communications, Analytical Chemistry, 020901 industrial engineering & automation, Secure communication, Control theory, Synchronization (computer science), circular multi state synchronization, 0202 electrical engineering, electronic engineering, information engineering, lcsh:TP1-1185, Electrical and Electronic Engineering, Instrumentation, Parametric statistics, Lyapunov stability, adaptive-robust control, multi-state synchronization, Estimation theory, business.industry, Atomic and Molecular Physics, and Optics, time-delayed chaotic systems, 020201 artificial intelligence & image processing, business
Abstract

In this paper, the multi-state synchronization of chaotic systems with non-identical, unknown, and time-varying delay in the presence of external perturbations and parametric uncertainties was studied. The presence of unknown delays, unknown bounds of disturbance and uncertainty, as well as changes in system parameters complicate the determination of control function and synchronization. During a synchronization scheme using a robust-adaptive control procedure with the help of the Lyapunov stability theorem, the errors converged to zero, and the updating rules were set to estimate the system parameters and delays. To investigate the performance of the proposed design, simulations have been carried out on two Chen hyper-chaotic systems as the slave and one Chua hyper-chaotic system as the master. Our results showed that the proposed controller outperformed the state-of-the-art techniques in terms of convergence speed of synchronization, parameter estimation, and delay estimation processes. The parameters and time delays were achieved with appropriate approximation. Finally, secure communication was realized with a chaotic masking method, and our results revealed the effectiveness of the proposed method in secure telecommunications.

Published
2021

27. Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review

Author

U. Rajendra Acharya, Marjane Khodatars, Maryam Panahiazar, Saeid Nahavandi, Jónathan Heras, Afshin Shoeibi, Juan Manuel Górriz, Mitra Rezaei, Parisa Moridian, Fahime Khozeimeh, Roohallah Alizadehsani, and Mahboobeh Jafari

Subjects
FOS: Computer and information sciences, Magnetic Resonance Spectroscopy, Multiple Sclerosis, Computer science, Computer Vision and Pattern Recognition (cs.CV), Feature extraction, Computer Science - Computer Vision and Pattern Recognition, Health Informatics, Feature selection, Deep Learning, Neuroimaging, Artificial Intelligence, medicine, FOS: Electrical engineering, electronic engineering, information engineering, Preprocessor, Humans, Modalities, medicine.diagnostic_test, business.industry, Deep learning, Image and Video Processing (eess.IV), Pattern recognition, Magnetic resonance imaging, Electrical Engineering and Systems Science - Image and Video Processing, Magnetic Resonance Imaging, Computer Science Applications, Computer-aided diagnosis, Artificial intelligence, business
Abstract

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided.

Published
2021
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