Key result
An HRV-based seizure prediction algorithm achieved an average sensitivity of 78.59% and a false prediction rate of 0.21/Hr, demonstrating superiority to a random predictor.
Why the study?
Does an HRV-based prediction algorithm accurately predict epileptic seizures in epileptic patients?
Observational (n=16)
Does an HRV-based prediction algorithm accurately predict epileptic seizures in epileptic patients?
Effect estimate: FPR 0.21/Hr
An HRV-based algorithm demonstrates potential for predicting epileptic seizures with an average sensitivity of 78.59% and a low false prediction rate.
HRV seizure prediction remains investigational; leaves open its clinical utility pending prospective validation.
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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Behbahani et al. (2016) conducted an observational in Epileptic seizures (n=16). Heart Rate Variability (HRV) analysis algorithm vs. Random predictor was evaluated on Seizure prediction (sensitivity and false prediction rate) (FPR 0.21/Hr). An HRV-based seizure prediction algorithm achieved an average sensitivity of 78.59% and a false prediction rate of 0.21/Hr, demonstrating superiority to a random predictor.
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