Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/68586
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dc.contributor.authorAhmed Amir
dc.date.accessioned2016-05-27T07:00:52Z
dc.date.available2016-05-27T07:00:52Z
dc.date.issued2016
dc.identifier.urihttp://hdl.handle.net/10356/68586
dc.description.abstractThe most common primary brain diseases characterized by the recurrence of seizures with unpredictable onsets is epilepsy. The random nature of seizure significantly affects the quality of life for people with epilepsy and although some causes of epilepsy are known, the majority of causes of epilepsy are still unknown. Epilepsy are defined by large scale abnormal neuronal activity with high degree of correlation between neighboring potential changes. So far work has been done on studying focal and generalized epilepsy by taking into account the spatial-temporal characteristics of epileptic seizure. More work has to be done in understanding the structural brain connectivity change during inter-ictal state (resting state) and ictal state (in the form of high amplitude oscillation). We have investigated the changes in dynamics of the system (computational model of the brain obtained through clinical DTI data) by altering the connections in the structure as well as stimulating the nodes (brain regions) in the model. The spatial heterogeneity is taken into account by extending the neural population model and the role of Thalamocortical population is highlighted in the project. We have used the structural connections, clinically inferred from DTI data of l7 patients and 17 healthy subjects. The abnormality is investigated using complex network measures (Graph Theory) of brain connectivity. Also we have explored the phenomenological aspect of epileptic seizure and examine the abnormality in the brain structure by calculating the mean escape time of the nodes (time required for transition between inter-ictal and ictal state). By examining the network change and finding the influential nodes (brain regions), it can help the neurosurgeons to plan the resection more accurately (optimal surgery) and can reduce the likelihood of further seizures.en_US
dc.format.extent52 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleNon linear modelling and control of epileptic seizuresen_US
dc.typeThesis
dc.contributor.supervisorJustin Dauwelsen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
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