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Article

Online Prediction of Lead Seizures from iEEG Data

1
Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA
2
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Tombini Mario and Giovanni Pellegrino
Brain Sci. 2021, 11(12), 1554; https://doi.org/10.3390/brainsci11121554
Received: 3 October 2021 / Revised: 6 November 2021 / Accepted: 23 November 2021 / Published: 24 November 2021
(This article belongs to the Special Issue Advances in Seizure Prediction and Detection)
We describe a novel system for online prediction of lead seizures from long-term intracranial electroencephalogram (iEEG) recordings for canines with naturally occurring epilepsy. This study adopts new specification of lead seizures, reflecting strong clustering of seizures in observed data. This clustering results in fewer lead seizures (~7 lead seizures per dog), and hence new challenges for online seizure prediction, that are addressed in the proposed system. In particular, the machine learning part of the system is implemented using the group learning method suitable for modeling sparse and noisy seizure data. In addition, several modifications for the proposed system are introduced to cope with the non-stationarity of a noisy iEEG signal. They include: (1) periodic retraining of the SVM classifier using most recent training data; (2) removing samples with noisy labels from training data; and (3) introducing a new adaptive post-processing technique for combining many predictions made for 20 s windows into a single prediction for a 4 h segment. Application of the proposed system requires only two lead seizures for training the initial model, and results in high prediction performance for all four dogs (with mean 0.84 sensitivity, 0.27 time-in-warning, and 0.78 false-positive rate per day). The proposed system achieves accurate prediction of lead seizures during long-term test periods, 3–16 lead seizures during a 169–364 day test period, whereas earlier studies did not differentiate between lead vs. non-lead seizures and used much shorter test periods (~few days long). View Full-Text
Keywords: iEEG; non-stationarity; lead seizure; seizure prediction; support vector machines; unbalanced classification; group learning iEEG; non-stationarity; lead seizure; seizure prediction; support vector machines; unbalanced classification; group learning
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MDPI and ACS Style

Chen, H.-H.; Shiao, H.-T.; Cherkassky, V. Online Prediction of Lead Seizures from iEEG Data. Brain Sci. 2021, 11, 1554. https://doi.org/10.3390/brainsci11121554

AMA Style

Chen H-H, Shiao H-T, Cherkassky V. Online Prediction of Lead Seizures from iEEG Data. Brain Sciences. 2021; 11(12):1554. https://doi.org/10.3390/brainsci11121554

Chicago/Turabian Style

Chen, Hsiang-Han, Han-Tai Shiao, and Vladimir Cherkassky. 2021. "Online Prediction of Lead Seizures from iEEG Data" Brain Sciences 11, no. 12: 1554. https://doi.org/10.3390/brainsci11121554

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