A Bayesian change point model for epileptic seizure detection

Epilepsy is a chronic neurological disorder in which the normal pattern of neuronal activity in the brain becomes disturbed. Identification of the brain region that is abnormally active during an epileptic seizure is vital for epilepsy surgery. One way of achieving so is to collect electroencephalography (EEG) signals from epileptic people and then to identify the active region as a seizure occurs. In this work, we present a Bayesian change point model that detects when seizures occur. We applied our method to a data set that contains 48 EEG and electrocardiography (EKG) record pairs collected from epileptic people and observed that the model is able to detect all seizures.