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Investigation of Brain Connectivity Changes during Seizure using Graph Theory

Khoshkhah Tinat, Atefeh | 2020

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 53542 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Karbalai Aghajan, Hamid; Mohammadzadeh, Hoda
  7. Abstract:
  8. Epilepsy is a chronic neurological disorder characterized by recurrent and abrupt seizures. Seizures occur due to disturbances in the interactions between the distributed neuronal populations in the brain. Investigation of the brain functional connectivity networks is a way to better understand how the brain functions during seizure. To estimate the brain functional connectivity network, we need criteria that can estimate the functional connections between the brain regions from the recorded brain functional data such as electroencephalogram (EEG) signals. After estimating the functional brain connectivity networks, it is possible to create graphs corresponding to these estimated networks and evaluate the graph theory-based topological features.In this study, we applied functional connectivity measures to find synchronization between different regions of the brain during the pre-seizure, seizure and post-seizure periods. The aim of the study is to evaluate changes in the functional network of the brain in terms of synchronization during the seizure. For this purpose, four measures such as coherence, phase locking value, imaginary part of coherency and phase lag index have been used to estimate functional brain connectivity during the pre-seizure, seizure and post-seizure periods. Then, the topological features of the estimated networks have been extracted in these three periods by using graph based measures. In the final step, we have investigated the functional connectivity changes of the brain in five different frequency bands during seizures using some tools from statistics and machine learning fields.The results show that the rate of brain synchrony in the seizure period is higher than the pre-seizure and post-seizure periods in the theta, alpha and beta bands and decreases in the gamma bands. These synchronization changes also cause changes in the topological features of functional networks during seizures. Statistical analyses have also shown that there is a statistically significant difference between the rate of change of network state among seizure period with pre-seizure and post-seizure periods only in gamma bands and in these frequency bands, the brain network changes significantly at a slower rate during the seizure period than the pre-seizure and post-seizure periods
  9. Keywords:
  10. Epilepsy ; Graph Theory ; Electroencephalogram Signals Classification ; Seizure Prediction ; Functional Brain Connectivity Network ; Statistical Tools

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