Seizure onset predicts its type

Summary

Objective

Epilepsy is characterized by transient alterations in brain synchronization resulting in seizures with a wide spectrum of manifestations. Seizure severity and risks for patients depend on the evolution and spread of the hypersynchronous discharges. With standard visual inspection and pattern classification, this evolution could not be predicted early on. It is still unclear to what degree the seizure onset zone determines seizure severity. Such information would improve our understanding of ictal epileptic activity and the existing electroencephalogram (EEG)-based warning and intervention systems, providing specific reactions to upcoming seizure types. We investigate the possibility of predicting the future development of an epileptic seizure during the first seconds of recordings after their electrographic onset.

Methods

Based on intracranial EEG recordings of 493 ictal events from 26 patients with focal epilepsy, a set of 25 time and frequency domain features was computed using nonoverlapping 1-second time windows, from the first 3, 5, and 10 seconds of ictal EEG. Three random forest classifiers were trained to predict the future evolution of the seizure, distinguishing between subclinical events, focal onset aware and impaired awareness, and focal to bilateral tonic–clonic seizures.

Results

Results show that early seizure type prediction is possible based on a single EEG channel located in the seizure onset zone with correct prediction rates of 76.2 ± 14.5% for distinguishing subclinical electrographic events from clinically manifest seizures, 75 ± 16.8% for distinguishing focal onset seizures that are or are not bilateral tonic–clonic, and 71.4 ± 17.2% for distinguishing between focal onset seizures with or without impaired awareness. All predictions are above the chance level (P < .01).

Significance

These findings provide the basis for developing systems for specific early warning of patients and health care providers, and for targeting EEG-based closed-loop intervention approaches to electrographic patterns with a high inherent risk to become clinically manifest.

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