Epileptic Seizure Prediction Based on Multivariate Statistical Process Control of Heart Rate Variability Features
We proposed a new epileptic seizure prediction method utilizing heart rate variability (HRV) analysis. It monitors time-frequency-domain HRV features for predicting seizures by using multivariate statistical process control (MSPC). The application results to clinical data produced accurate predictions (91%) for epileptic seizures and there were few false-positives (0.7 times/hour). The possibility of realizing a HRV-based epileptic seizure prediction system was shown.