A review of significant research on epileptic seizure detection and prediction using heart rate variability.

Epilepsy is a brain disorder that many people struggle with all over the world. Despite extensive research, epilepsy is still an important challenge without a clear solution. There may be confusion about providing a specific approach due to the variety of epileptic seizures and the effectiveness in different environmental conditions. Some patients with epilepsy undergo treatment through medication or surgery. Epileptic patients suffer from unpredictable conditions that may occur at any moment. Given the origins of these seizures, researchers have focused on predicting epileptic seizures via electroencephalogram (EEG). The results indicate some success in this regard. This success led to a focus on optimizing these methods and the evaluation of epilepsy seizure prediction through other vital signals. Both sympathetic and parasympathetic inhibitory effects are undeniable during epileptic seizures. This conflict is visible in the change in heart rate. In recent years several investigations have focused on a behavioral study of heart rate changes before the seizures. The results have led to the development of algorithms for classifying and predicting epileptic seizures using the electrocardiogram (ECG) and the more distinct heart rate variability (HRV). This article presents an overview of seizure detection and prediction methods and discusses their potential to improve the quality of life of epileptic patients.