Key result
An HRV-based seizure prediction method using multivariate statistical process control predicted seizures with 91% sensitivity and a false positive rate of 0.7 per hour.
Why the study?
Does an HRV-based anomaly monitoring technique predict epileptic seizures in patients with epilepsy?
Observational (n=14)
Does an HRV-based anomaly monitoring technique predict epileptic seizures in patients with epilepsy?
Effect estimate: Sensitivity 91%
An HRV-based anomaly monitoring technique demonstrates high sensitivity for predicting epileptic seizures, suggesting potential for wearable sensor-based prediction systems.
May enable wearable HRV seizure forecasting in epilepsy; leaves open prospective validation before clinical adoption.
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. RESULTS: We applied the proposed method to the clinical data collected from 14 patients. In the collected data, 8 patients had a total of 11 awakening preictal episodes and the total length of interictal episodes was about 57 h. The application results of the proposed method demonstrated that seizures in ten out of eleven awakening preictal episodes could be predicted prior to the seizure onset, that is, its sensitivity was 91%, and its false positive rate was about 0.7 times per hour. CONCLUSION: This study proposed a new HRV-based epileptic seizure prediction method, and the possibility of realizing an HRV-based epileptic seizure prediction system was shown. SIGNIFICANCE: The proposed method can be used in daily life, because the heart rate can be measured easily by using a wearable sensor.
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Fujiwara et al. (2015) conducted an observational in Epilepsy (n=14). HRV-based epileptic seizure prediction method was evaluated on Seizure prediction prior to onset (Sensitivity 91%). An HRV-based seizure prediction method using multivariate statistical process control predicted seizures with 91% sensitivity and a false positive rate of 0.7 per hour.
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