Heart rate variability features for epilepsy seizure prediction

Abstract

Although refractory epileptic patients suffer from uncontrolled seizures, their quality of life (QoL) may be improved if an epileptic seizure can be predicted in advance. In the preictal period, an excessive neuronal activity of epilepsy affects the autonomic nerve system. Since the fluctuation of the R-R interval (RRI) of an electrocardiogram (ECG), called heart rate variability (HRV), reflects the autonomic nervous function, an epileptic seizure may be predicted through monitoring HRV data of an epileptic patient. In the present work, preictal and interictal HRV data of epileptic patients were analyzed for developing an epilepsy seizure prediction system. The HRV data of five patients were collected, and their HRV features were calculated. The analysis results showed that frequency HRV features, such as LF and LF/HF, changed at least one minute before seizure onset in all seizure episodes. The possibility of realizing a HRV-based seizure prediction system was shown through these analysis.