Research Area:  Machine Learning

Abstract:

Epileptic seizures are a principal brain dysfunction with important public health implications, as they affect 0.8% of humans. Many of these patients (20%) are resistant to treatment with drugs1. The ability to anticipate the onset of seizures in such cases would permit clinical interventions. The view of chronic focal epilepsy now is that abnormally discharging neurons act as pacemakers to recruit and entrain other normal neurons by loss of inhibition and synchronization into a critical mass2. Thus, pre-ictal changes should be detectable during the stages of recruitment. Traditional signal analyses, such as the count of focal spike density3, the frequency coherence4 or spectral analyses are not reliable predictors. Non-linear indicators may undergo consistent changes around seizure onset5,6,7. Our objective was to follow the transition into seizure by reconstructing intracranial recordings in implanted patients as trajectories in a phase space and then introduce non-linear indicators to characterize them8,9. These indicators take into account the extended spatio–temporal nature of the epileptic recruitment processes10 and the corresponding physiological events governed by short-term causalities in the time series. We demonstrate that in most cases (17 of 19), seizure onset could be anticipated well in advance (between 2–6 minutes beforehand), and that all subjects seemed to share a similar route towards seizure.

Keywords:  

Author(s) Name:  J. Martinerie, C. Adam, M. Le Van Quyen, M. Baulac, S. Clemenceau, B. Renault & F.J. Varela

Publisher name:  Springer Nature

DOI:  10.1038/2667

Volume Information:  volume 4, pages1173–1176 (1998)

Paper Link:   https://www.nature.com/articles/nm1098_1173