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Anticipating the unobserved: Prediction of subclinical seizures
University of Freiburg.
University Hospital Freiburg.
UPMC Paris 6.
University of Coimbra.
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2011 (English)In: Epilepsy & Behavior, ISSN 1525-5050, E-ISSN 1525-5069, Vol. 22, S119-S126 p.Article in journal (Refereed) Published
Abstract [en]

Subclinical seizures (SCS) have rarely been considered in the diagnosis and therapy of epilepsy and have not been systematically analyzed in studies on seizure prediction. Here, we investigate whether predictions of subclinical seizures are feasible and how their occurrence may affect the performance of prediction algorithms. Using the European database of long-term recordings of surface and invasive electroencephalography data, we analyzed the data from 21 patients with SCS, including in total 413 clinically manifest seizures (CS) and 3341 SCS. Based on the mean phase coherence we investigated the predictive performance of CS and SCS. The two types of seizures had similar prediction sensitivities. Significant performance was found considerably more often for SCS than for CS, especially for patients with invasive recordings. When analyzing false alarms triggered by predicting CS, a significant number of these false predictions were followed by SCS for 9 of 21 patients. Although currently observed prediction performance may not be deemed sufficient for clinical applications for the majority of the patients, it can be concluded that the prediction of SCS is feasible on a similar level as for CS and allows a prediction of more of the seizures impairing patients, possibly also reducing the number of false alarms that were in fact correct predictions of CS. less thanbrgreater than less thanbrgreater thanThis article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Place, publisher, year, edition, pages
Elsevier , 2011. Vol. 22, S119-S126 p.
Keyword [en]
Subclinical seizure, Electrographic seizure, Epilepsy, Seizure prediction, Mean phase coherence, Random predictor
National Category
Medical and Health Sciences
URN: urn:nbn:se:liu:diva-74160DOI: 10.1016/j.yebeh.2011.08.023ISI: 000298067800018OAI: diva2:480852
Funding Agencies|European Union|211713|German Federal Ministry of Education and Research (BMBF)|01GQ0420|German Federal Government||State Government||German Science Foundation|Ti 315/4-2|Baden-Wurttemberg Stiftung||Available from: 2012-01-20 Created: 2012-01-20 Last updated: 2012-03-25

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