Epilepsy is a very prevalent neurological disorder. The gold standard in diagnosis of epilepsy is the EEG signal recorded during a seizure with characteristic ictal pattern. Automated systems for detection of seizures are a field of intensive research, in an attempt to create a reproducible, observer-independent mechanism for epilepsy diagnosis. Chronic therapy is a cornerstone of the epilepsy treatment, but the possibility to predict seizure onset and, consequently, to act with medications right before the seizure, instead of relying on everyday medications, is considered the holy grail of epilepsy research. Significant element of morbidity and mortality in epilepsy is sudden unexpected death in epilepsy (SUDEP) that occurs in roughly 1% of patients. Signal analysis techniques for EEG have been a staple in epilepsy research, but recently, with the rise of telemetric systems, heart rate variability (HRV) analysis derived from the ECG signal has been gaining importance. It has been found that perturbations in autonomic nervous system (ANS) regulation occur during, and even up to several minutes before, seizure onset allowing for changes in HRV to act in prediction, as well as detection, of seizures. Also, there is a compelling research exploring the extent of autonomic disbalance during seizures, as well as in the interictal periods in patients at risk for or that have had SUDEP. The focus of this review is to give a short crossection of research involving the utility HRV has in prediction and detection of seizure onset, as well as determining etiology classification and risk evaluation in SUDEP
