P371 Simulation and estimation of Hawkes processes for modeling EEG data to forecast epileptic seizures

Abstract

Objectives Prediction of epileptic seizures has been in the forefront of the research for a long time but without the development of a satisfactory solution so far. Our aim is to develop a forecasting method – previously applied in seismology – that is based on fitting so-called Hawkes processes to EEG data, both exhibiting self-exiting dynamics. The choice of this approach is justified by the observation of Sornette and Osorio, according to which earthquakes and epileptic seizures show numerous similarities in their dynamics. Methods As an essential preliminary work for model-fitting we first implemented an algorithm for the simulation of self-exiting point processes. This was followed by the implementation of a maximum likelihood estimation (MLE) method based on the work of Ozaki. Results By visual inspection of projections of the three-dimensional parameter space we found a variety of behaviors of the cost function including some degenerate constellations of the parameters, where the behavior of the cost function relies almost solely on one parameter. Despite the simplicity of the local optimization method applied during the off-line estimation of the parameters, we succeeded to estimate the parameters with a fairly good approximation. Discussion and conclusion Based on our preliminary results, we conclude that the simulation of Hawkes-processes and fitting them to a point-process derived from EEG data is promising. As a next step, we plan to implement an on-line MLE method, by which real-time change detection in the EEG-derived parameters becomes possible. Significance As far as we know, self-exiting point processes have not been applied in seizure prediction before.