Seizure Prediction with Machine Learning using Real and Simulated Electrocorticography Data

Epilepsy is the most common chronic neurological disorder, affecting approximately one percent of people worldwide. Patients with symptoms not well controlled with medication often suffer significantly reduced quality of life due to the unpredictable nature of seizures, which are periods of pathological synchronization of neural activity in the brain. Using a surgically-implanted intracranial electrode grid, electrocorticography (ECoG) provides better spatial and temporal resolution of brain electrical activity, compared with conventional scalp electroencephalography (EEG). We combine this patient data with simulated output from a full Hodgkin-Huxley calculation using in silico neurons connected with a small-world network topology. Supervised Machine Learning, a set of powerful and flexible artificial intelligence techniques that allow computers to classify complex data without the need for explicit programming, along with topological data analysis methods, are employed with a goal of developing an algorithm that can be used for the real-time clinical prediction of seizure risk.