Coherent False Seizure Prediction in Epilepsy, Coincidence or Providence?

TL;DR

This study reveals that false and missing seizure alarms are largely due to intrinsic data changes rather than classifier limitations, suggesting a shift in hypothesis could improve prediction performance.

Contribution

It demonstrates that intrinsic data variability, not classifier flaws, limits seizure prediction accuracy and proposes a new perspective on preictal state modeling.

Findings

Algorithms show consistent performance across individuals.

False predictions are positively correlated between methods.

Excluding test samples based on second method improves results.

Abstract

Seizure forecasting using machine learning is possible, but the performance is far from ideal, as indicated by many false predictions and low specificity. Here, we examine false and missing alarms of two algorithms on long-term datasets to show that the limitations are less related to classifiers or features, but rather to intrinsic changes in the data. We evaluated two algorithms on three datasets by computing the correlation of false predictions and estimating the information transfer between both classification methods. For 9 out of 12 individuals both methods showed a performance better than chance. For all individuals we observed a positive correlation in predictions. For individuals with strong correlation in false predictions we were able to boost the performance of one method by excluding test samples based on the results of the second method. Substantially different algorithms exhibit a highly consistent performance and a strong coherency in false and missing alarms. Hence, changing the underlying hypothesis of a preictal state of fixed time length prior to each seizure to a proictal state is more helpful than further optimizing classifiers. The outcome is significant for the evaluation of seizure prediction algorithms on continuous data.