Tree based Ensemble for Enhanced Prediction (TEEP) of epileptic seizures

Abstract

Accurate and timely prediction of seizures can improve the quality of life of epileptic patients to a huge extent. This work presents a seizure prediction model that performs data extraction and feature engineering to enable effective demarcation of preictal signals from interictal signals. The proposed Tree based Ensemble for Enhanced Prediction (TEEP) model is composed of three major phases; the feature extraction phase, feature selection phase and the prediction phase. The data is preprocessed, and features are extracted based on the nature of the data. This enables the prediction algorithm to perform time-based predictions. Further, statistical features are also extracted, followed by the process of feature aggregation. The resultant data is passed to the feature selection module to identify the attributes that exhibit highest correlation with the prediction variable. Incorporation of these two modules enhances the generalization capability of the TEEP model. The resultant features are passed to the boosted ensemble model for training and prediction. The TEEP model is analyzed using the Epileptic Seizure Recognition Data from University Hospital of Bonn and the NIH Seizure Prediction data from Melbourne University, Australia. Results from both the datasets indicate effective performances. Comparisons with the existing state-of-the-art models in literature exhibits the enhanced prediction levels of the TEEP model.