dc.contributor.authorGupta Payal
dc.date.accessioned2016-05-31T03:50:55Z
dc.date.available2016-05-31T03:50:55Z
dc.date.issued2016-05-31
dc.identifier.urihttp://hdl.handle.net/10356/68723
dc.description.abstractEpilepsy is a chronic disease associated with recurrent seizure activities in the brain. It is one of the common brain diseases prevailing in 1% of the world population. Moreover, its significant treatment costs, both financially and in terms of mortality have attracted Iots of researchers to dig alternative methods of diagnosis and its treatment. Epileptic activity in the brain arises from dysfunctional neuronal networks involving cortical and sub-cortical grey matter as well as their connections via white matter fibers. Physiological brain networks can be affected by the structural abnormalities causing the epileptic activity, or by the epileptic activity itself. The identification of pathological and physiological networks in an individual subject is critical for the planning of epilepsy surgery aiming at resection or at least interruption of the epileptic network while sparing physiological networks which have potentially been re-modeled by the disease. Until now, there has been a lot of studies on functional connectivity of the epileptic brain network. Very little has been studied in the area of structural connectivity. In our work, we have used the structural connections, clinically inferred from DTI data of 15 patients and 15 healthy subjects. Using this data, we investigated the abnormalities existing in the brain structure as compared to the healthy brain and also explored the statistical correlation between seizure frequency and the brain structure. The methods used in this dissertation are two in silico techniques. These techniques may help doctors in faster determination of the affected brain region.en_US
dc.format.extent61 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleEpileptic seizures as an emergent phenomenon of pathalogical network structureen_US
dc.typeThesis
dc.contributor.supervisorJustin Dauwelsen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMSC(COMPUTER CONTROL and AUTOMATION)en_US


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