Enhanced extra trees classifier for epileptic seizure prediction

For a machine learning based epileptic seizure prediction, it is important for the model to be implemented in small and implantable or wearable devices. These devices can be used to monitor the epileptic patients. However, the current state-of-the-art seizure prediction methods are complex and computationally intensive. We use SHapley Additive exPlanation (SHAP) to find relevant intracranial electroencephalogram (iEEG) features and improve the computational efficiency of a state-of-the-art seizure prediction method based on the extra trees classifier while maintaining prediction performance. Results for a small contest dataset and a much larger dataset with continuous recordings of up to 3 years per patient from 15 patients yield better than chance prediction performance (p < 0.004). Moreover, while performance of the SHAP-based model is comparable to that of the benchmark, the overall training and prediction time of the model has been reduced by a factor of 1.83. It can also be noted that of the feature called zero crossing value is the best EEG feature for seizure prediction. These results suggest state-of-the-art seizure prediction performance can be achieved using efficient methods based on optimal feature selection.