Nedderman Hall, 105

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Speaker Hmayag Partamian of the Department of Bioengineering will present "Machine Learning on Interictal Intracranial EEG Predicts Surgical Outcome in Drug Resistant Epilepsy."



Abstract:

Surgical success for patients with focal drug resistant epilepsy (DRE) relies on accurate localization of the epileptogenic zone (EZ). Currently, no exam delineates this zone unambiguously. Instead, the EZ is approximated by the area where seizures begin, which is identified manually through a tedious process that is prone to errors and biases. More importantly, resection of this area does not always predict good surgical outcome. Here, we propose an artificially intelligent, patient-specific framework that automatically identifies the EZ requiring little to no input from clinicians, without waiting for a seizure to occur. The framework transforms intracranial electroencephalography data into spatiotemporal representations of brain activity discriminating the epileptogenic networks from the background activity. The epileptogenic network delineates the EZ with high precision and predicts surgical outcome. The framework eliminates the need for manual data inspection and enhances surgical planning for patients with drug resistant epilepsy.


The spotlight seminar is open to the UTA Community. Refreshments will be served at 3:45 p.m.