Seizure forecasting: Where do we stand?

Author:

Andrzejak Ralph G.1ORCID,Zaveri Hitten P.2ORCID,Schulze‐Bonhage Andreas3ORCID,Leguia Marc G.1ORCID,Stacey William C.45ORCID,Richardson Mark P.6ORCID,Kuhlmann Levin7ORCID,Lehnertz Klaus8910ORCID

Affiliation:

1. Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain

2. Department of Neurology Yale University New Haven Connecticut USA

3. Epilepsy Center, Neurocenter University Medical Center, University of Freiburg Freiburg Germany

4. Department of Neurology, Department of Biomedical Engineering BioInterfaces Institute, University of Michigan Ann Arbor Michigan USA

5. Division of Neurology VA Ann Arbor Medical Center Ann Arbor Michigan USA

6. School of Neuroscience Institute of Psychiatry Psychology and Neuroscience, King's College London London UK

7. Department of Data Science and AI, Faculty of Information Technology Monash University Clayton Victoria Australia

8. Department of Epileptology University of Bonn Medical Centre Bonn Germany

9. Helmholtz Institute for Radiation and Nuclear Physics University of Bonn Bonn Germany

10. Interdisciplinary Center for Complex Systems University of Bonn Bonn Germany

Abstract

AbstractA lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures—ICTALS 2022—convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long‐suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi‐day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large‐scale network disorder yielded novel perspectives on the pre‐ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure‐forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well‐being.

Funder

Medical Research Council Centre for Neurodevelopmental Disorders

National Health and Medical Research Council

National Institutes of Health

Universität Bern

Publisher

Wiley

Subject

Neurology (clinical),Neurology

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