فهرست مطالب الیاس ابراهیم زاده

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.

In countering an enemy's military, intelligence and security provocations, applying passive defense tools is inevitable. In today's world, applying passive defense to counter these issues in regard to less cost and low vulnerability and the ease of its implementation is the most essential tool and something that has always been noticed. Recently, regarding the high scientific, technical and military advances, these techniques have emerged in a modern manner. One of the most important systems in passive defense is the authenticity or certainty in detecting the truthfulness of a person's words through brain signal processing. In this essay, with the intent of presenting a capable method to detect the guilty person in the lie detectors, a method based on recognition parameter called P300 has been designed and reviewed. For this reason and based on the Odd-ball Pattern(reconstruction of hidden figure) a test has been designed and the brain signals of 18 people have been recorded. And finally Guilty and Innocent persons were classified through Bootstrapped Correlation Difference (BCD) method. The validated results show the promise of the proposed approach by the accuracy of 94.5%.

EEG is one of the most important and common sources for study of brain function and neurological disorders. Automated systems are under study for many years to detect EEG changes. Because of the importance of making correct decision، we are looking for better classification methods for EEG signals. In this paper a smart compound system is used for classifying EEG signals to different groups. Since in each classification the system accuracy of making decision is very important، in this study we look for some methods to improve the accuracy of EEG signals classification. In this paper the use of Mixture of Experts for improving the EEG signals classification of normal subjects and patients with epilepsy is shown and the classification accuracy is evaluated. Decision making was performed in two stages: 1) feature extractions with different methods of eigenvector and 2) Classification using the classifier trained by extracted features. This smart system inputs are formed from composites features that are selected appropriate with network structure. In this study tree methods based on eigenvectors (Minimum Norm، MUSIC، Pisarenko) are chosen for the estimation of Power Spectral Density (PSD). After the implementation of ME and train it on composite features، we propose that this technique can reach high classification accuracy. Hence، EEG signals classification of epilepsy patients in different situations and control subjects is available. In this study، Mixture of Experts structure was used for EEG signals classification. Proper performance of Neural Network depends on the size of train and test data. Combination of multiple Neural Networks even without using the probable structure in obtaining weights in classification problem can produce high accuracy in less time، which is important and valuable in the classification point of view.

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.

In Brain-based lie detection systems which have been recently introduced as substitutes for classic lie detection systems، the procedure for recognition of guilty and innocent subjects is done by inspection of brain signals which are acquired during the specific Polygraph test. With the aim of increasing the performance، this paper presents a powerful method for detection of Guilty persons in lie detection systems using brain signals. It was an experimental study. The employed method is based on the extraction of P300 components from brain signals. In this way، the test protocol was designed based on Odd-ball method، firstly. This test was done on 16 people and their brain signals were acquired. After preprocessing، P300 amplitude was extracted for each person from brain signals، and finally Guilty and Innocent persons were classified by comparing amplitude through Bootstrapped Amplitude Difference (BAD) method. The obtained results show that the proposed method has detected correctly 7 out of 8 guilty persons and 8 out of 8 innocent persons. Also، the validated results show the promise of the proposed approach in discrimination of guilty subjects from innocent subjects by the accuracy of 93. 75%. Knowing the existence of precious information in brain signals and their relation with brain''s cognitive activities and also considering the performance of the proposed method، there are enough reasons to use the proposed approach for detection of guilty persons from innocent ones. Further، in comparison with previous methods، the impact of man ability to control brain signal parametersand creating incorrect feelings has been reduced through the proposed method.