Seizure Forecasting by High-Frequency Activity (80–170 Hz) in Long-term Continuous Intracranial EEG Recordings

Abstract

Background

Reliable seizure forecasting has important implications in epilepsy treatment and improving the quality of lives for people with epilepsy. High-frequency activity (HFA) is one biomarker that has received significant attention over the past two decades, but its predictive value in seizure forecasting remains uncertain. This work aimed to determine the utility of HFA in seizure forecasting.

Methods

We used seizure data and HFA (80-170 Hz) data obtained from long-term, continuous intracranial EEG recordings of drug-resistant epilepsy patients. Instantaneous rates and phases of HFA cycles were used as features for seizure forecasting. Seizure forecasts based on each individual HFA feature, and using a combined approach, were generated pseudo-prospectively (causally). To compute the instantaneous phases for pseudo-prospective forecasting, real-time phase estimation based on an autoregressive model was employed. Features were combined using a weighted average approach. The performance of seizure forecasting was primarily evaluated by the area under the curve (AUC).

Results

Of 15 studied patients (median recording duration: 557 days, median seizures: 151), 12 patients with more than 10 seizures after 100 recording days were included in the pseudo-prospective analysis. The presented real-time phase estimation is feasible and can causally estimate the instantaneous phases of HFA cycles with high accuracy. Pseudo-prospective seizure forecasting based on HFA rates and phases performed significantly better than chance in 11 out of 12 patients, although there were patient-specific differences. Combining rate and phase information improved forecasting performance compared to using either feature alone. The combined forecast using the best-performing channel yielded a median AUC of 0.70, a median sensitivity of 0.57, and a median specificity of 0.77.

Conclusion

These findings show that HFA could be useful for seizure forecasting and represent proof of concept for utilizing prior information of patient-specific relationships between HFA and seizures in pseudo-prospective forecasting. Future seizure forecasting algorithms might benefit from the inclusion of HFA, and the real-time phase estimation approach can be extended to other biomarkers.

Authors

Zhuying Chen, Matias I. Maturana, Anthony N. Burkitt, Mark J. Cook, and David B. Grayden.