Coherent false seizure prediction in epilepsy, coincidence or providence?

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung



Objective: 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. Methods: We evaluated two algorithms on three datasets by computing the correlation of false predictions and estimating the information transfer between both classification methods. Results: 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. Conclusions: 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. Significance: The outcome is significant for the evaluation of seizure prediction algorithms on continuous data.


Seiten (von - bis)157-164
FachzeitschriftClinical neurophysiology : journal of the International Federation of Clinical Neurophysiology
PublikationsstatusVeröffentlicht - 5 Nov. 2021

Externe IDs

PubMed 34844880
ORCID /0000-0001-9875-3534/work/110199772
unpaywall 10.1016/j.clinph.2021.09.022
Mendeley f62a30f4-d7a8-34bc-a275-15d60d2c682b
ORCID /0000-0001-7436-0103/work/142240240



  • Classification, Deep learning, Intracranial EEG, Seizure forecast, Seizure prediction, Neural Networks, Computer, Seizures/diagnosis, Humans, Middle Aged, Male, Electroencephalography, Forecasting, Epilepsy/diagnosis, Adult, Female, Aged, Databases, Factual