Sharif Digital Repository

Since recording first electroencephalogram (EEG) of human brain in 1929 until now it becomes as a powerful tool in neuroscience. At first information extraction was done by visionary approaches only. But because of some problems in the context of inaccuracy and also in analyzing data different methods were proposed in order to extract hidden information of EEG. Among these approaches Fourier transformation was suggested as a very useful method that could draw out so many characteristics of signal different frequency components. However this way had many faults that cause limitation in analyzing time series and as a result other methods have been considered. One method that later has been... 

In this thesis, the efficiency of bump modeling has been investigated on brain signals, in a variety of aspects including analysis, detection, classification and prediction. The aim of bump modeling is to provide an optimized representation of the signal in time-frequency domain. This would be done by discriminating oscillatory bursts from background signal and then showing them by half-ellipsoid functions called bump. Consequently, the problem of dealing with large numbers of parameters and hence complicated calculations, which are serious concerns in similar methods, can be overcome. This is in addition to the benefits of using time-frequency representation of the signal.The aim of bump... 

Complex systems are composed of a large number of subsystems behaving in a collective manner. In such systems, which are usually far from equilibrium, collective behavior arises due to self-organization and results in the formation of temporal, spatial, spatio-temporal and functional structures. The dynamics of order parameters in complex systems are generally non-stationary and can interact with each other in nonlinear manner. As a result, the analysis of the behavior of complex systems must be based on the assessment of the nonlinear interactions, as well as the determination of the characteristics and the strength of the fluctuating forces. This leads to the problem of retrieving a... 

Complex systems are composed of a large number of subsystems behaving in a collective manner. In such systems, which are usually far from equilibrium, collective behavior arises due to self-organization and results in the formation of temporal, spatial, spatio-temporall structures. Examples of complex systems are turbulent flow, stock markets, dynamics of a brain, etc. In study of the complex systems, we always encounter with handling and analysing of a Big-Data set. There are several approaches to overcome this problem, among which the most powerful method is the clustering analysis. Clustering algorithm is based on the classifying of dynamics of complex system using some similarity... 

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... 

In the process of recording electrical activity of the brain, the signal of interest is usually contaminated with different activities arising from various sources of noise and artifact such as muscle activity. This renders denoising as an important preprocessing stage in some ElectroEncephaloGraphy (EEG) applications such as source localization. In this thesis, we propose six methods for noise cancelation of epileptic signals. The first two methods, which are based on Generalized EigenValue Decomposition (GEVD) and Denoising Source Separation (DSS) frameworks, are used to denoise interictal data. To extract a priori information required by GEVD and DSS, we propose a series of preprocessing... 

Epilepsy is a neurological disorder whose prevalence is estimated to be 1% of the world population. Electroencephalogram (EEG) is one of the best and convenient non-invasive tools used in diagnosis and analysis of this disease. Epileptic components extracted from EEG recordings are widely used in neuroscience in the diagnosis analysis like epilepsy source localization. However, epileptic components are often contaminated and covered with artifacts of physiological origin (baseline, EMG, ECG, EOG, etc.) or instrument noises (power supply, electrode, etc.). So, preprocessing and denoising is necessary for precise analysis of epilepsy EEG recording. Heretofore, several methods have been... 

EEG Signal Processing has really important role in study of nerves system and nervous diseases. In most applications, signals occur in the frequency band ranging below 100 Hz are considered. In this study, the higher frequency components of the signal, especially in patients with epilepsy, reported which they are called high frequency oscillations (HFOs). Some sources considered that HFOs are up to 1000 Hz and some other considered up to 2500 Hz. In study of HFOs, the high frequencies between 100 and 200 Hz that are called ripple and seen in normal mode are separated from the faster frequencies from 200 to 500 Hz and these components can accurately help to detect epileptic foci generator (to... 

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... 

Abnormality detection and prognosis of epileptic patients with artificial intelligence and machine learning techniques is still in its early experimental stages. Surgical candidacy determination for epilepsy depends on the clinical actions which involve an intracranial electrode implantation followed by prolonged electrographic monitoring (EEG phase II) .This invasive test is very costly, painful and time consuming. Here the goal is integration of the two following paradigms: 1-Non invasive multimodality data of epilepsy. 2- Artificial intelligence and machine learning techniques. We have used human brain multi-modality database system that includes patient’s demographics, clinical and EEG... 

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... 

The processing of brain signals, including the electrocorticogram (ECoG) signal, is widely used in the investigation of neurological diseases. Conventionally, the ECoG signal has frequency components up to the range of 80 Hz. Studies have proven that in some conditions, such as epilepsy, the brain signal includes frequency components higher than 80 Hz, which are called high-frequency oscillations (HFO). Therefore, HFOs are recognized as a biomarker for epilepsy. The aim of this thesis is to review the previous methods of detecting HFOs and to present new methods with greater efficiency in the direction of diagnosis or treatment of epileptic patients. For this purpose, we used the ECoG data... 

Centuries passed and the human knew himself as the protagonist who searches around nature and discovers the phenomena. But after the birth of ``neuroscience", his wisdom and the process of reasoning were also added to the list of uncovered subjects. Since its arrival, many scientists started investigating ``reasoning", "sleep", ``memory disorders" etc. with a such framework. One of the main branches of this stream is the ``Synchronization" problem when the neurons get synced in the matter of spiking likelihood. ``Synchronization" means a lot to the community, because it is said that it is one major symptom of Epilepsy. With that said, we need to get to the root of this effect. It seems... 

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... 

In most of brain studies, the primary objective is to find dipole activities, an underdetermined problem that requires additional constraints. Adequate constraints can be added by using information from other modalities. This research aims to develop a platform that combines various noninvasive modalities to improve localization accuracy. To accomplish this, two novel general approaches to combining modalities are proposed. In the first approach, the result of localizing by different methods and in different modalities are processed and combined in intervals by Dempster Shaffer's combination law. The final amount of bipolar activity is obtained by cumulating the activities obtained at... 

Complex systems involve a large number of degrees of freedom and consist of many components. Interactions of these components with each other, or with an external force, play a significant role in the collective behavior of the complex system.We come across complex systems in many different fields of study including neuroscience, climatology, studying stock markets, etc. The non-linearity of the interactions between their components is what they have in common. Interesting macro-scale properties can be observed in a complex system, as a result of the collective behavior of the system components. We usually focus on studying a group of components in a system, rather than a single component,... 

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... 

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... 

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... 

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...