Sharif Digital Repository

Epilepsy is one of the most common non-communicable neurological disorders, characterized by recurrent seizure symptoms. Although much progress has been made in the diagnosis, control, and treatment of epilepsy in recent years, the exact mechanism of seizures, the specific method for early diagnosis of epilepsy and related syndromes, and definitive treatment for all patients are not yet known. In a type of seizure known as focal seizure, the electrical activity of neurons at the epilepsy focus synchronizes abnormally, and this synchronization can propagate to other regions of the brain in a process called secondary generalization, which finding a method for its prevention is our essential goal... 

This thesis presents my Ph.D. research study focusing on the dynamic analysis of magnetic nanoparticles (MNPs) for epilepsy and blood-brain barrier (BBB) applications. In this analysis, we took into account various parameters including the magnetic field, fluid behavior, geometry and material of MNPs. Based on this computational study, the generated magnetic field in epileptic foci results in the aggregation of nanoparticles. This may offer the advantage of using MNPs as a Magnetic Resonance Imaging (MRI) contrast agent. Furthermore, in this study, we also demonstrated and discussed the advantage of MNPs for crossing BBB using the external magnetic field. The outcome of this research project... 

The prevalence of epilepsy in the world and the need for surgery to treat patients have made it essential to locate the site of epilepsy before surgery. One method is to apply source localization algorithms to the EEG signals of epileptic patients in the ictal and interictal periods. However, because these signals are contaminated with various noises, they are challenging to interpret and require noise cancellation. Therefore, various methods have been proposed to eliminate the noise. Among these methods, a new method recently used to remove noise from the epileptic signal is Morphological Component Analysis (MCA). This method uses the basic concepts of sparse representation of signals to... 

Epilepsy is one of the most common neurological disorders, second only to stroke, with a prevalence of 0.6–0.8% of the world’s population. Epilepsy is not cured, Two-thirds of the patients achieve sufficient seizure control from anticonvulsive medication, and another 8–10% could benefit from respective surgery. For the remaining 25% of patients, no sufficient treatment is currently available. In the recent decent, many studies in this field attempt to predict the onset time of a impending seizure by monitoring the biomedical signals, Electroencephalogram (EEG) Signal processing is one of these biomedical Signals that many In this work, First we talk about the basic concepts of Epilepsy ,... 

Epilepsy is one of the diseases that affects the quality of life of epileptic patients. Epileptic patients lose control during epileptic seizures and are more likely to face problems. Designing and creating a seizure detection system can reduce casualties from epileptic attacks. In this study, we present an automatic method that reduces the artifact from the raw signals, and then classifies the seizure and non-seizure epochs. At all stages, it is assumed that no information is available about the patient and this detection is made only based on the information of other patients. The data from this study were recorded in Temple Hospital and the recording conditions were not controlled, so... 

After Alzheimer and brain attack, the most common neurological disorder is epilepsy, which often involves seizures. In two-thirds of patients with epilepsy, the seizures can be controlled by antiepileptic drugs, and about 8% of patients can use epilepsy surgery; but unfortunately there is no acceptable treatment for the other 25% of these patients. Therefore preventing from epilepsy losses is a very important topic.
The gold standard for the diagnosis of the epilepsy is EEG monitoring. In this method, electrodes are placed on the scalp. Electrodes are uncomfortable to wear, and cause invasion to the patient, hence long-term monitoring and home monitoring is not feasible. In some... 

Different perceptual, cognitive, and emotional situations results in a kind of information flow in the brain by means of coordinated neuronal oscillations. Analysing these oscillations, especially synchronizations of different brain regions, can illustrate the brain response to the aforementioned situations. In the literature, connectivity between brain regions is divided into the three groups of structural, effective, and functional, s.t. the first one referes to the connectivity between nearby regions, while the second and third ones focus on the synchronization of oscillations of arbitrary located regions. Although EEG is not the best choice for analyzing functional connectivity between... 

Epilepsy is one of the most significant neurological disorders that about one percent of people suffer from it. Epilepsy can only be controlled, and so far no cure for it has been provided. Despite the many advances in the treatment of diseases, for a quarter of patients there is no medical treatment solution for controlling epileptic seizures. In the studies of medical groups on the epilepsy, one approach is employment of some models for each type of epilepsy. These types may be created in the animals to allow studying of the mechanism of epilepsy and also finding drugs of treatment or controlling seizures for each type of epilepsy. There is a type of epilepsy that is called absence... 

Epilepsy is a chronic neurological disorder characterized by recurrent and abrupt seizures. Seizures occur due to disturbances in the interactions between the distributed neuronal populations in the brain. Investigation of the brain functional connectivity networks is a way to better understand how the brain functions during seizure. To estimate the brain functional connectivity network, we need criteria that can estimate the functional connections between the brain regions from the recorded brain functional data such as electroencephalogram (EEG) signals. After estimating the functional brain connectivity networks, it is possible to create graphs corresponding to these estimated networks... 

As the fourth most common neurological disorder, epilepsy affects lots of people all around the world, some of whom have to live with unpredictable seizures uncontrollable by surgery or medication. Hence, Developing systems for detection and prediction of the epileptic seizures will help the patients to avoid the possible damages caused by sudden seizures. This study addresses the task of epileptic seizure prediction, using three different novel approaches. The first approach, which is based on anomaly detection, contains three steps: feature extraction from EEG signals, training a one-class SVM classifier, and a post-processing step. The second method exploits a recurrent neural network to...