Analysis of structural and functional differences between epileptic patients and healthy controls

• Main Author: Murali Sivaprasanth
• Other Authors: Justin Dauwels
• Format: Theses and Dissertations
• Language: English
• Published: 2015
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Medical electronics
• Online Access: http://hdl.handle.net/10356/65098
• Institution: Nanyang Technological University

As per the reports of the World Health Organization, around 50 million people worldwide are affected by Epilepsy which constitutes close to 1% of the total human population. In 70% of the cases, the use of AED (Anti-Epilepsy drugs) has proven to be successful in the treatment of epilepsy. In some cases who respond poorly to the Anti-Epilepsy Drugs, surgical therapy may be effective. Surgical epilepsy treatment is a complicated procedure which requires a thorough analysis of the patient's brain activity. This is usually done by obtaining data through various neuroimaging techniques such as electroencephalogram (EEG), magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), etc. and then identifying the location of the seizure onset zone which may differ from patient to patient. Once the seizure onset zone has been located, it can be removed through resection to prevent the occurrence of seizures. However, in case of generalized seizures, the onset zone for the seizures is unknown which makes surgical treatment impossible. In this dissertation, actual clinical data of patients with IGE have been analysed to identify the differences present in the structural and the functional organisation of nodes and edges in the brain networks of patients when compared to that of healthy controls by calculating and comparing various graph theoretical measures. The significantly different regions identified may help doctors to perform surgical epilepsy treatment in patients with IGE. The results may assist further research to unravel the structural and functional abnormalities that cause excessive neuronal activity in the brain.