CISUC

Anticipating the unobserved: Prediction of subclinical seizures

Authors

Hinnerk Feldwirsch-Drentrup
MAtthias Ihle
Michel LeVanQuyen
César Teixeira
António Dourado
Jens Timmer
Francisco Sales
Vincent Navarro
Andreas Schulze Bonhage
Bjoern Schelter

Abstract

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. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Keywords

Subclinical seizure; Electrographic seizure; Epilepsy; Seizure prediction; Mean phase coherence; Random predictor

Related Project

EPILEPSIAE- Evolving Platform for Improving Living Expectation of Patients

Journal

Epilepsy & Behaviour, Vol. 22, #1, pp. 119-126, -, December 2011

Cited by

Year 2016 : 2 citations

 Lehnertz, K., Dickten, H., Porz, S., Helmstaedter, C. and Elger, C.E., 2016. Predictability of uncontrollable multifocal seizures–towards new treatment options. Scientific reports, 6.

 Jin, B., Wang, S., Yang, L., Shen, C., Ding, Y., Guo, Y., Wang, Z., Zhu, J., Wang, S. and Ding, M., 2016. Prevalence and predictors of subclinical seizures during scalp video-EEG monitoring in patients with epilepsy. International Journal of Neuroscience, pp.1-8.

Year 2015 : 4 citations

 Collaborating and sharing data in epilepsy research
JB Wagenaar, GA Worrell, Z Ives… - Journal of Clinical …, 2015 - journals.lww.com
Summary: Technological advances are dramatically advancing translational research in
Epilepsy. Neurophysiology, imaging, and metadata are now recorded digitally in most
centers, enabling quantitative analysis. Basic and translational research opportunities to ...

 Assessing directionality and strength of coupling through symbolic analysis: an application to epilepsy patients
K Lehnertz, H Dickten - … Transactions of the Royal …, 2015 - rsta.royalsocietypublishing.org
Abstract Inferring strength and direction of interactions from electroencephalographic (EEG)
recordings is of crucial importance to improve our understanding of dynamical
interdependencies underlying various physiological and pathophysiological conditions in ...

 Early Seizure detection Algorithm Based on Intracranial EEG and Random Forest Classification
C Donos, M Dümpelmann… - International Journal of …, 2015 - World Scientific
The goal of this study is to provide a seizure detection algorithm that is relatively simple to
implement on a microcontroller, so it can be used for an implantable closed loop stimulation
device. We propose a set of 11 simple time domain and power bands features, computed ...
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