Seizure tracking of epileptic EEGs using a model-driven approach.

Abstract

As a chronic neurological disorder, epilepsy is characterized by recurrent and unprovoked epileptic seizures that can disrupt the normal neuro-biologic, cognitive, psychological conditions of patients. Therefore, it is worthwhile to give a detailed account of how the epileptic EEG evolves during a period of seizure so that an effective control can be guided for epileptic patients in clinics. Considering the successful application of the neural mass model (NMM) in exploring the insights into brain activities for epilepsy, in this paper, we aim to construct a model-driven approach to track the development of seizures using epileptic EEGs. We first propose a new time-delay Wendling model with sub-populations (TD-W-SP model) with respect to three aspects of improvements. Then we introduce a model-driven seizure tracking approach, where a model training method is designed based on extracted features from epileptic EEGs and a tracking index is defined as a function of the trained model parameters. Numerical results on eight patients on CHB-MIT database demonstrate that our proposed method performs well in simulating epileptic-like EEGs as well as tracking the evolution of three stages (that is, from pre-ictal to ictal and from ictal to post-ictal) during a period of epileptic seizure. A useful attempt to track epileptic seizures by combining the NMM with the data analysis.