Ali Ekhlasi

Bio: Ali Ekhlasi is an academic researcher from Islamic Azad University. The author has contributed to research in topics: Transfer entropy & Attention deficit hyperactivity disorder. The author has an hindex of 2, co-authored 10 publications receiving 10 citations.

Direction of information flow between brain regions in ADHD and healthy children based on EEG by using directed phase transfer entropy

Abstract: Directed information flow between brain regions might be disrupted in children with Attention Deficit Hyperactivity Disorder (ADHD) which is related to the behavioral characteristics of ADHD. This paper aims to investigate the different information pathways of brain networks in children with ADHD in comparison with healthy subjects. EEG recordings were obtained from 61 children with ADHD and 60 healthy children without neurological disorders during attentional visual task. Effective connectivity among all scalp channels was calculated using directed phase transfer entropy (dPTE) for delta, theta, alpha, beta, and lower-gamma frequency bands. Group differences were evaluated using permutation tests in connectivity between regions. Significant posterior to anterior patterns of information flow in theta frequency bands were found in healthy subjects (p-value < 0.05), while disrupted pattern flow, in an opposite way, was found in ADHD children. In the beta band, information flow in pathways between anterior regions was significantly higher in healthy individuals than in the ADHD group. These differences are more indicated in connectivity that leads from frontal and central regions to the right frontal regions of the brain (F8 electrode). Furthermore, connections from central and lateral parietal areas to Pz electrode areas are statistically significant and higher in healthy children in this band. In the delta band, internal connections in the anterior region show a significant difference between the two groups, as this amount is higher in the ADHD group. Our analysis may provide new insights into information flow in brain regions of ADHD children in comparison with healthy children.

Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals

Abstract: Purpose: The present study was conducted to investigate and classify two groups of healthy children and children with Attention Deficit Hyperactivity Disorder (ADHD) by Effective Connectivity (EC) measure. Since early detection of ADHD can make the treatment process more effective, it is important to diagnose it using new methods. Materials and Methods: For this purpose, Effective Connectivity Matrices (ECMs) were constructed based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children of the same age. ECMs of each individual were obtained by the directed Phase Transfer Entropy (dPTE) between each pair of electrodes. ECMs were calculated in five frequency bands including, delta, theta, alpha, beta, and gamma. Based on ECM, an Effective Connectivity Vector (ECV) was constructed as a feature vector for the classification process. Furthermore, ECV of different frequency bands was pooled in one global ECV (gECV). Multilayer Artificial Neural Network (ANN) was used in the steps of classification and feature selection by the Genetic Algorithm (GA). Results: The highest classification accuracy with the selected features of ECV was related to theta frequency band with 89.7%. After that, the delta frequency band had the highest accuracy with 89.2%. The results of ANN classification and GA on the gECV reported 89.1% of accuracy. Conclusion: Our findings show that the dPTE measure, which determines effective connectivity between the brain regions, can be used to classify between ADHD and healthy groups. The results of the classification have improved compared to some studies that used the functional connectivity measures.

Types of EMD Algorithms

Abstract: In this paper, we present an algorithm based on the Empirical Mode Decomposition (EMD) method and compare it with two other existing EMD algorithms. EMD is a data-driven method that used as a propelling tool for analyzing and decomposing non-stationary and non-linear data without any previous assumption like choosing mother function in Wavelet. EMD generates a finite and often small number of Intrinsic Mode Functions (IMF). The decomposition is based on the direct extraction of the energy-related to various intrinsic time scales, an important parameter of the system. We first introduce the EMD method and its limitations, two existing EMD algorithms, and compare the two algorithms. Then, an improved EMD algorithm is presented. The three algorithms are tested on several time functions with different frequency characteristics. Results show the proposed algorithm provides no spurious IMF for these functions and is not placed in an infinite loop. Finally, the three algorithms are applied to analyze an ECG signal. Results reveal the presented algorithm is an appropriate algorithm to be used for analyzing biological signals.

Analysis of ECG signals to classify abnormal patterns by employing Artificial Neural Network and Discrete Wavelet Coefficients

Abstract: Electrocardiogram (ECG) is one of the most important tools to diagnose the heart health. Patients with arrhythmias diagnosed by ECG are commonly seen in clinical practice. Incorrectly interpreted ECGs might result in inappropriate clinical decisions. It is necessary to reduce physicians' pressure and increase accuracy in diagnostic and therapeutic processes with automatic diagnosis of various cardiac disorders and arrhythmias. The following article suggests classifying the different types of cardiac arrhythmias from ECG signals. The arrhythmia classification is based on the morphological features of the signal and features extracted by a Discrete Wavelet Transform (DWT). The Artificial Neural Network (ANN) has been used as a classifier. MIT-BIH ECG arrhythmia database acquired from pysionet.org is used for analysis purposes. ANN is used to classify four groups, including three types of arrhythmias, Atrial Premature Beat (APB), Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), and Normal group (N). The K-fold cross-validation method results show that using morphological features and wavelet coefficients together, the average classification accuracy is 93%. Average classification accuracy is 83% when only the signal's morphological features are used, and if only the wavelet coefficients are used, it is 88%. In this way, the highest classification accuracy is obtained using both categories of features.

A computer-aided approach for automatic detection of breast masses in digital mammogram via spectral clustering and support vector machine

Abstract: Breast cancer continues to be a widespread health concern all over the world Mammography is an important method in the early detection of breast abnormalities In recent years, using an automatic Computer-Aided Detection (CAD) system based on image processing techniques has been a more reliable interpretation in the illustration of breast distortion In this study, a fully process-integrated approach with developing a CAD system is presented for the detection of breast masses based on texture description, spectral clustering, and Support Vector Machine (SVM) To this end, breast Regions of Interest (ROIs) are automatically detected from digital mammograms via gray-scale enhancement and data cleansing The ROIs are segmented as labeled multi-sectional patterns using spectral clustering by the means of intensity descriptors relying on the region’s histogram and texture descriptors based on the Gray Level Co-occurrence Matrix (GLCM) In the next step, shape and probabilistic features are derived from the segmented sections and given to the Genetic Algorithm (GA) to do the feature selection The optimal feature vector comprising a fusion of selected shape and probabilistic features is submitted to linear kernel SVM for robust and reliable classification of mass tissues from the non-mass Linear discrimination analysis (LDA) is also performed to ascertain the significance of the nominated feature space The classification results of the proposed approach are presented by sensitivity, specificity, and accuracy measures, which are 895%, 912%, and 90%, respectively

A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms

Abstract: Breast cancer is one of the most common death causes amongst women all over the world. Early detection of breast cancer plays a critical role in increasing the survival rate. Various imaging modalities, such as mammography, breast MRI, ultrasound and thermography, are used to detect breast cancer. Though there is a considerable success with mammography in biomedical imaging, detecting suspicious areas remains a challenge because, due to the manual examination and variations in shape, size, other mass morphological features, mammography accuracy changes with the density of the breast. Furthermore, going through the analysis of many mammograms per day can be a tedious task for radiologists and practitioners. One of the main objectives of biomedical imaging is to provide radiologists and practitioners with tools to help them identify all suspicious regions in a given image. Computer-aided mass detection in mammograms can serve as a second opinion tool to help radiologists avoid running into oversight errors. The scientific community has made much progress in this topic, and several approaches have been proposed along the way. Following a bottom-up narrative, this paper surveys different scientific methodologies and techniques to detect suspicious regions in mammograms spanning from methods based on low-level image features to the most recent novelties in AI-based approaches. Both theoretical and practical grounds are provided across the paper sections to highlight the pros and cons of different methodologies. The paper’s main scope is to let readers embark on a journey through a fully comprehensive description of techniques, strategies and datasets on the topic.

Automatic Breast Tumor Diagnosis in MRI Based on a Hybrid CNN and Feature-Based Method Using Improved Deer Hunting Optimization Algorithm.

Abstract: Breast cancer is an unusual mass of the breast texture. It begins with an abnormal change in cell structure. This disease may increase uncontrollably and affects neighboring textures. Early diagnosis of this cancer (abnormal cell changes) can help definitively treat it. Also, prevention of this cancer can help to decrease the high cost of medical caring for breast cancer patients. In recent years, the computer-aided technique is an important active field for automatic cancer detection. In this study, an automatic breast tumor diagnosis system is introduced. An improved Deer Hunting Optimization Algorithm (DHOA) is used as the optimization algorithm. The presented method utilized a hybrid feature-based technique and a new optimized convolutional neural network (CNN). Simulations are applied to the DCE-MRI dataset based on some performance indexes. The novel contribution of this paper is to apply the preprocessing stage to simplifying the classification. Besides, we used a new metaheuristic algorithm. Also, the feature extraction by Haralick texture and local binary pattern (LBP) is recommended. Due to the obtained results, the accuracy of this method is 98.89%, which represents the high potential and efficiency of this method.

EPSPatNet86: eight-pointed star pattern learning network for detection ADHD disorder using EEG signals

Abstract: Objective. The main objective of this work is to present a hand-modelled one-dimensional signal classification system to detect Attention-Deficit Hyperactivity Disorder (ADHD) disorder using electroencephalography (EEG) signals. Approach. A novel handcrafted feature extraction method is presented in this research. Our proposed method uses a directed graph and an eight-pointed star pattern (EPSPat). Also, tunable q wavelet transforms (TQWT), wavelet packet decomposition (WPD), statistical extractor, iterative Chi2 (IChi2) selector, and the k-nearest neighbors (kNN) classifier have been utilized to develop the EPSPat based learning model. This network uses two wavelet decomposition methods (TQWT and WPD), and 85 wavelet coefficient bands are extracted. The proposed EPSPat and statistical feature creator generate features from the 85 wavelet coefficient bands and the original EEG signal. The learning network is termed EPSPatNet86. The main purpose of the presented EPSPatNet86 is to detect abnormalities of the EEG signals. Therefore, 85 wavelet subbands have been generated to extract features. The created 86 feature vectors have been evaluated using the Chi2 selector and the kNN classifier in the loss value calculation phase. The final features vector is created by employing a minimum loss-valued eight feature vectors. The IChi2 selector selects the best feature vector, which is fed to the kNN classifier. An EEG signal dataset has been used to demonstrate the presented model’s EEG signal classification ability. We have used an ADHD EEG dataset since ADHD is a commonly seen brain-related ailment. Main results. Our developed EPSPatNet86 model can detect the ADHD EEG signals with 97.19% and 87.60% accuracy using 10-fold cross and subject-wise validations, respectively. Significance. The calculated results demonstrate that the presented EPSPatNet86 attained satisfactory EEG classification ability. Results show that we can apply our developed EPSPatNet86 model to other EEG signal datasets to detect abnormalities.