Loading...
| Friend's email | |
| Your name | |
| Your email | |
| enter code | |
This page was sent successfuly
- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 39445 (05)
- University: Sharif University of Technology
- Department: Electrical Engineering
- Advisor(s): Shamsollahi, Mohammad Bagher
- Abstract:
- 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 , Seizure prediction, and then we reviewed the recent works in this field. In this thesis we used the Data of seizure prediction competition (held in 2003, Bonn) that are consist of long pre surgery recordings of 21 patients that the start and stop of the seizures are labeled. In this work, at first some of the most successful measures that could reach better results are studied and modified in some cases, and a new measure is presented. For each measure, statistical information of measures distribution for Preictal and interictal periods is presented. In other section of the thesis, we used the Liley EEG model as a dynamical model of EEG. Two parameters of the model which are candidates for change during an epileptic seizure are defined as new states in state space representation of this dynamical model. Then SIS particle filter is applied for estimating the defined states over time using the recorded epileptic EEG as the observation of the system. A method for fast numerical solution of the nonlinear coupled equation of the model is proposed. This Model is used for tracking the dynamical properties of brain during epileptic seizure. Tracking the changes of these new defined states of the model have good information about the state transition of the model (interictal/preictal/ictal) and can be used in online monitoring algorithms for predicting seizures in epilepsy. And at the end, for the first time in this field we defined a new combinational index based on FIS (Fuzzy inference system) for predicting the epileptic seizures. The results show a promising increase compared the other presented measures
- Keywords:
- Dynamics Models ; Epileptic Seizure Prediction ; Liley Dynamical Model ; Mean Phase Coherency ; Cross Dynamical Similarity ; Effective Correlation Dimension ; Increment of Accumulated Energy
Digital Object List-
محتواي پايان نامه - view
Bookmark- project_final
- Abstract.pdf