A General NAR Model for Seizure Identification Across Different Patients

Abstract

Data-driven and dynamic models have been proposed to better understand and describe epileptiform activities. However, there is still a need for more comprehensive ones suitable for individual seizures from patients but also general across them, taking into account common features. Thus, this work proposes an alternative nonlinear autoregressive model to address this issue. Essentially, the regression coefficients obtained from individual models are taken as random variables, and a new one is constructed based on their respective median values. The results show that the proposed approach is effective in describing all of the individual seizures with a single model, yielding low estimation errors. This finding is useful when large datasets are not available, and can be adopted in the context of seizure prediction and attenuation in a similar way to that of transfer learning applications.