Seizure Prediction in Epilepsy

1. Introduction 2. From Single Cells to Complex Networks in Seizure Generation 2.1 Theoretical Approaches 2.2 Practical Approaches 3. Seizure Detection 3.1 Frameworks for Assessing Seizure Detection 3.2 Suggested Techniques and their Performance 4. Seizure Prediction 4.1 Frameworks for Assessing Seizure Prediction 4.2 Suggested Techniques and their Performance 5. Conclusion

Jens Timmer holds a Chair in Theoretical Physics and its Applications in the Life Sciences at the Physics Institute of the University of Freiburg, Germany, where he also leads the research group "Data Analysis and Modeling of Dynamic Processes in the Life Sciences". He obtained his academic degrees from the University of Freiburg. Professor Timmer has authored more than 150 scientific publications. He is specialized in developing methods from nonlinear dynamics, stochastic processes, and statistics, and applies them in the Life Sciences ranging from basic research in cell biology to clinical applications in neurology.

Andreas Schulze-Bonhage is head of the Epilepsy Center at the University Hospital Freiburg, Germany. Having obtained his academic degrees at the Universities of Münster and Freiburg, he trained in epileptology in Bonn, Montreal, and Cleveland before founding the Epilepsy Center in Freiburg. Professor Schulze-Bonhage has authored more than 80 original publications in peer-reviewed neurological and neuroscience journals. He is a member of several German societies, faculty member of the Bernstein Center for Computational Neuroscience in Freiburg, and member of the subcommission on clinical neurophysiology of the ILAE.

Björn Schelter is a member of the research group "Data Analysis and Modeling of Dynamic Processes in the Life Sciences", led by Professor Jens Timmer at the Freiburg Center for Data Analysis and Modeling, University of Freiburg, Germany. Dr. Schelter obtained his academic degrees at the University of Freiburg. He has authored 20 publications. His research mainly focuses on the development and interdisciplinary application of multivariate time series analysis techniques.