Forecasting and classification of seizures based on physiological cycles

This thesis investigates the use of physiological cycles from long-term continuous electroencephalography (EEG), electrocardiography (ECG), and heart rate (HR) data to forecast and classify seizures. The promising results suggest that cyclic features extracted from multi-modal physiological data could be effective for the seizure forecasting and classification applications.