An Adaptive Pattern Learning Framework to Personalize Online Seizure Prediction

Abstract

The sudden and spontaneous occurrence of epileptic seizures can impose a significant burden on patients with epilepsy. If seizure onset can be prospectively predicted, it could greatly improve the life of patients with epilepsy and also open new therapeutic avenues for epilepsy treatment. However, discovering effective predictive patterns from massive brainwave signals is still a challenging problem. The prediction of epileptic seizures is still in its early stage. Most existing studies actually investigated the predictability of seizures offline instead of a truly prospective online prediction, and also the high inter-individual variability was not fully considered in prediction. In this study, we propose a novel adaptive pattern learning framework with a new online feature extraction approach to achieve personalized online prospective seizure prediction. In particular, a two-level online feature extraction approach is applied to monitor intracranial electroencephalogram (EEG) signals and construct a pattern library incrementally. Three prediction rules were developed and evaluated based on the continuously-updated patient-specific pattern library for each patient, including the adaptive probabilistic prediction (APP), adaptive linear-discriminant-analysis-based prediction (ALP), and adaptive Naive Bayes-based prediction (ANBP). The proposed online pattern learning and prediction system achieved impressive prediction results for 10 patients with epilepsy using long-term EEG recordings. The best testing prediction accuracy averaged over the 10 patients were 79, 78, and 82 percent for the APP, ALP, and ANBP prediction scheme, respectively. The experimental results confirmed that the proposed adaptive prediction framework offers a promising practical tool to solve the challenging seizure prediction problem.