Epileptic Seizure Prediction Based on Multivariate Statistical Process Control of Heart Rate Variability Features

 Epileptic Seizure Prediction Based on Multivariate Statistical Process Control of Heart Rate Variability Features

 Objective: The present study proposes a new epileptic seizure prediction method through integrating heart rate variability (HRV) analysis and an anomaly monitoring technique. Methods: Because excessive neuronal activities in the preictal period of epilepsy affect the autonomic nervous systems and autonomic nervous function affects HRV, it is assumed that a seizure can be predicted through monitoring HRV. In the proposed method, eight HRV features are monitored for predicting seizures by using multivariate statistical process control, which is a well-known anomaly monitoring method.