Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Online Prediction of Lead Seizures from iEEG Data

Version 1 : Received: 3 October 2021 / Approved: 7 October 2021 / Online: 7 October 2021 (08:21:21 CEST)

A peer-reviewed article of this Preprint also exists.

Chen, H.-H.; Shiao, H.-T.; Cherkassky, V. Online Prediction of Lead Seizures from iEEG Data. Brain Sci. 2021, 11, 1554. Chen, H.-H.; Shiao, H.-T.; Cherkassky, V. Online Prediction of Lead Seizures from iEEG Data. Brain Sci. 2021, 11, 1554.

Journal reference: Brain Sci. 2021, 11, 1554
DOI: 10.3390/brainsci11121554

Abstract

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, 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 non-stationarity of noisy iEEG signal. They include: (1) periodic re-training of SVM classifier using most recent training data; (2) removing samples with noisy labels from training data; (3) introducing new adaptive post-processing technique for combining many predictions made for 20-second windows into a single prediction for 4 hr segment. Application of the proposed system requires only 2 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). Proposed system achieves accurate prediction of lead seizures during long-term test periods, 3–16 lead seizures during 169–364 days test period, whereas earlier studies did not differentiate between lead vs. non-lead seizures and used much shorter test periods (~few days long).

Keywords

iEEG; non-stationarity; lead seizure; seizure prediction; support vector machines; unbalanced classification; group learning

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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