Machine Learning Methods in Seizure Prediction and Forecasting: What Is the Best Approach?

Traditional treatments do not work on 33% of epileptic patients.Warning devices employing seizure prediction or forecasting algorithms could bring patients a newfound quality of life. These algorithms would attempt to detect the preictal period, a transitional moment between regular brain activity and the seizure, and warn the user. Several past methodologies have been developed, triggering an alarm when detecting the preictal period, but few have been clinically applicable. Recent studies have suggested a paradigm change to seizure forecasting that takes a probabilistic approach instead of the crisp one of seizure prediction. The alarm is substituted by a constant risk assessment analysis. To the best of our knowledge, no direct comparison between prediction and forecasting using the same database has been made. This paper explores methodologies capable of seizure forecasting and compares them with seizure prediction ones. Using data from the EPILEPSIAE database, we developed several patient-specific prediction and forecasting algorithms with different classifiers (a Logistic Regression, a 15 Support Vector Machines ensemble, and a 15 Shallow Neural Networks ensemble). Results show an increase of the sensitivity in forecasting relative to prediction of up to 146% and in the number of patients displaying an improvement over chance of up to 200%. These results suggest that seizure forecasting may be more suitable for seizure warning devices than seizure prediction.