Key result
An ECG-based seizure prediction approach using manual thresholding of heart rate variability features achieved 100% sensitivity and 91-97% specificity across three databases, predicting over 40% of seizures more than 10 minutes before onset.
Why the study?
The study was conducted to quantify and predict changes in the ECG signal during the pre-ictal period prior to epileptic seizures.
Does an ECG-based heart rate variability threshold technique accurately predict epileptic seizures in patients with epilepsy?
Does an ECG-based heart rate variability threshold technique accurately predict epileptic seizures in patients with epilepsy?
An ECG-based algorithm utilizing heart rate variability features demonstrated high sensitivity and specificity for predicting epileptic seizures prior to their onset.
May enable pre-ictal ECG alerts in epilepsy; leaves open prospective validation before clinical adoption.
This study presents a new attempt to quantify and predict changes in the ECG signal in the pre-ictal period. In the proposed approach, threshold techniques were applied to the standard deviation (STD) of two Heart rate variability features (The number of heartbeats per two minutes and approximate entropy) computed to ensure prediction and quantification of the pre-ictal state. We analyzed clinical data taken from two epileptic public databases, Siena Scalp EEG and Post-Ictal Heart Rate Oscillations in Partial Epilepsy and a local database. By testing the proposed approach on the Siena scalp EEG database, we achieved a sensitivity of 100%, specificity of 95%, and an accuracy of 96.4% whereas using acquisitions from the post-Ictal database, we achieved a sensitivity of 100%, specificity of 91% and an accuracy of 94% and using the local database we achieved a sensitivity of 100%, a specificity of 97% and an accuracy of 97.5%. Furthermore, the proposed approach predicted 58.7%, 57.2, and 40% of the seizures before the onset by more than 10 min for the data taken from post-ictal, local and Siena database, respectively. Using the automatic threshold technique, we were able to achieve a sensitivity, specificity, and accuracy of 85%, 81%, 82% using our local database respectively, whereas using acquisitions take from the Siena Scalp EEG database, we achieved a sensitivity of 75%, specificity of 85% and an accuracy of 82%. Besides, using the post-ictal database, we achieved a sensitivity of 90%, a specificity of 83% and an accuracy of 85%.
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Mbarek et al. (2023) studied Epilepsy (n=71). ECG-based seizure prediction using threshold techniques on HRV features vs. Inter-ictal (non-seizure) periods was evaluated on Prediction of epileptic seizures (Sensitivity, Specificity, Accuracy). An ECG-based seizure prediction approach using manual thresholding of heart rate variability features achieved 100% sensitivity and 91-97% specificity across three databases, predicting over 40% of seizures more than 10 minutes before onset.
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