elias ebrahimzadeh

Since electroencephalography (EEG) signal contains temporal information and fMRI carries spatial information, we can reasonably expect that a combination of the two contributes greatly to precise localization of epileptic focuses. With that in mind, we have first extracted spike patterns from outside of scanner EEG, through detecting and averaging the interictal epileptiform discharges (IED). Then, having implemented the correlation between the identified pattern and inside-scanner EEG, an automated system was developed to extract the temporal information when an epileptic seizure is triggered. We proceeded to convolve the obtained regressor with the hemodynamic response function (HRF) using the general linear model (GLM) for the purpose of localizing the epileptic focus. This study was conducted on 6 medication-resistant patients with epilepsy whose data was recorded in the National Brain Mapping Lab (NBML). The results of the proposed method are in line with the information provided in EEG for each of the 6 patients, and for the 4 patients who were candidates for brain surgery, they provided further information. The results suggest a significant improvement in localization accuracy and precision compared to existing methods in the literature.

Affecting daily lives of millions of people, Epilepsy is a common central nervous system (neurological) disorder where cell activity in the brain is disturbed, causing recurrent seizures. Epilepsy can be treated commonly by medications. Be that as it may, medications do not always work as one may hope, and thus, some patients tend to resort to surgeries. The primary challenge in such surgeries, and by extension any other surgery where some part of brain may need to be disabled, disconnected or removed, is managing to pose no threat to the critical healthy textures adjacent or close to the part being operated on. Therefore, the precise localization of epileptic focus is a matter of vital importance in treating this condition. Various algorithms have been proposed to localize the brain sources and thus to determine the epileptic focus; however, none has yet been able to offer a solution to effectively address this issue. With EEG signal containing temporal information and fMRI carrying spatial information, it is hoped that the combination of the two can yield optimal results. In this research, we first remove the artifacts caused by EEG gradients, and proceed to study the signal in and outside the scanner by localizing the brain sources. The simultaneous processing of EEG-fMRI enables us to make use of the temporal information in EEG to analyze fMRI. Epileptic foci are finally localized based on GLM method. This study has been conducted on 10 medication-resistant patients with epilepsy whose data was recorded in Iran National Brain Mapping Centre. The results suggest a significant improvement in localization accuracy compared to existing methods in the literature.

Sudden Cardiac Death (SCD) is caused by loss of heart function which ultimately stops heart from pumping blood throughout the body and therefore, claims the patient’s life within few minutes. Once detected, sudden cardiac deaths could substantially decrease through applying medical procedures or instrumentations such as defibrillators. Nonetheless, effective approaches to SCD prediction, based on which doctors can make informed decisions, are yet to be discovered. This research aims to propose a novel approach to local feature selection with the assistance of the most accurate methodologies, which have formerly been developed in previous works of this team, for extracting features from nonlinear, time-frequency and classic processes. Furthermore, taking into consideration the existence of different features from different areas, the Mixture of Experts is put forward as a means of classification. The suggested methods enable us to select features that differ from one another in each minute before the incidence through the agency of optimal feature selection in each one-minute period of the signal. Not only will this facilitate increasing the prediction time from 4 minutes to 12 with a high level of accuracy, but it also will provide us with an opportunity to interpret clinical signs considering the plurality of features in each minute. Additionally, applying the Mixture of Experts classification proceeds to ensure a precise decision-making on the output of different areas processes. The results indicate to the superiority of the proposed method to those mentioned in similar studies.

Despite the high potential of distributed and renewable sources، their operation may cause problems because of their variability. Moreover، wind fluctuations or extreme weather changes may lead to temporary voltage fluctuations. Researches show that the energy storage can compensate this random nature effect and also it can be effective in a short duration، without requiring the load cut-off. Furthermore، utilizing the energy storing instruments provides more suitable conditions to use produced power and it can be considered as an economic solution. Therefore، in this paper a Smart Energy Management System has been designed in order to optimize the operation of a sample system، production planning، and energy storage. This study suggests the optimized method which can determine the optimized point depending on different goals and their relative effective coefficients. In this method، the usage time and the amount of usage of different energy sources have been determined so that the lowest cost and minimum environmental pollution has been achieved based on Pareto optimization. Eventually، in order to validate the proposed algorithm، this method has been implemented on an electrical propulsion sample system by MATLAB & GAMS software and related results are discussed.

Despite the significant decline in coronary artery disease (CAD) mortality in the second half of the 20th century، sudden cardiac death (SCD) continues to claim 250 000 to 300 000 US lives annually. Even in the presence of advanced first responder systems for resuscitation of out-of-hospital cardiac arrest، the overall survival rate in a recent North American analysis was 4. 6%. If there are existed suitable ways to predict sudden cardiac death، doctors can make better decisions for patients at risk. In this paper، we investigate a way to predict sudden cardiac death. To do this، after the extraction of the HRV signal from ECG signal، some nonlinear and time-frequency features have been extracted from HRV signal. Then، the dimension of the feature space is reduced by applying the feature selection and PCA. Finally، healthy people and people at risk of SCD are classified using an MLP neural network. To evaluate the capabilities of analytical methods in classification، we have compared the classification rates for nonlinear and TF features، separately and in combination. The results show that there are features in the HRV signal of SCD patients just near the occurrence of SCD، which is quite different from normal people. Also، results show that the combination of time-frequency and nonlinear features have a greater ability to detect this difference. It has also been investigated that there are precious information in four minutes before the incident of SCD to predict the death; and this is enough time to save the patient by doctors or medical centers.