Prediction of epileptic seizures based on heart rate variability

Until now, different approaches have been published to resolve the problem of predicting epileptic seizures. The results are reminiscent of a substantial need for improvements in these methods to reach the stage of the clinical application. Our aim is to develop a reliable epileptic seizure prediction algorithm based on the Heart Rate Variability (HRV) analysis.

We analyzed the HRV of sixteen epileptic patients with a total of 170 seizures, to predict the occurrence of seizures based on the dynamic changes of Electrocardiogram (ECG) during the pre-ictal period. Time and frequency-domain features were computed forthe consecutive time windows with a length of five minutes. An adaptive decision threshold method was used for raising alarms. Predictions were made when selected features exceeded the decision thresholds.

For the seizure occurrence period (SOP) of 4:30 minutes, and intervention time (IT) of 110 Sec, the presented method showed an average sensitivity of 78.59%, and average false prediction rate of 0.21/Hr, which indicates that the system has superiority to the random predictor.

The proposed approach shows a potential in the monitoring of epileptic patients and improving their life quality. The overall performance of the algorithm is a step forward for clinical implementation.