Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy

Abstract

Nearly one-third of patients with epilepsy continue to have seizures despite optimal medication management. Systems employed to detect seizures may have the potential to improve outcomes in these patients by allowing more tailored therapies and might, additionally, have a role in accident and SUDEP prevention. Automated seizure detection and prediction require algorithms which employ feature computation and subsequent classification. Over the last few decades, methods have been developed to detect seizures utilizing scalp and intracranial EEG, electrocardiography, accelerometry and motion sensors, electrodermal activity, and audio/video captures. To date, it is unclear which combination of detection technologies yields the best results, and approaches may ultimately need to be individualized. This review presents an overview of seizure detection and related prediction methods and discusses their potential uses in closed-loop warning systems in epilepsy.

Keywords: Accelerometry; Artificial neural network; Automated seizure detection; Closed-loop methods; ECG-based seizure detection; EEG-based seizure detection; Fourier; Higher-order spectra; Markov modeling; Support vector machine.