Topological Signal Processing in Seizure Localization

Dr. Wang earned a B.A. Honours degree in Mathematics at the University of Cambridge and an M.Phil specializing in the solvability of p-Laplacian differential equations via topological degree at the University of Hong Kong. She has recently defended her doctoral dissertation “Topological Data Analysis in Electroencephalographic Studies” at the University of Wisconsin – Madison and will begin her position as an Assistant Professor in Biostatistics at the University of South Carolina in the Fall. Her current methodological research is focused on topological signal processing and joint modeling of multimodal neuroimaging data.   

Epilepsy is a neurological disorder marked by sudden recurrent episodes of sensory disturbance, loss of consciousness, or convulsions, associated with abnormal electrical activity in the brain. Approximately one third of epilepsy patients do not respond to antiepileptic drug treatment and have to revert to surgical removal of the tissues generating seizures in the patient’s brain. Post-operative seizure freedom largely depends the accurate localization and delineation of the seizure-generating sites. Seizure localization is typically based on visual inspection by epileptologists of electroencephalographic (EEG) signals recorded at multiple sites on the scalp or brain of an epilepsy patient. Automated statistical algorithms will improve the chance of surgical success for patients without direct access to epileptologists. Due to the level of noise present in epileptic scalp EEGs, computational algorithms for seizure localization need to stay robust under