Epileptic Seizure Prediction in Scalp EEG Using an Improved HIVE-COTE Model

Epileptic seizures occur as a result of a disorder of brain waves. In this study, we aim at a patient-specific model for predicting the risk of seizure paroxysm by extracting the in-depth features from continuous scalp electroencephalographic(EEG) signals. Unfortunately, the false warning rates of generalized regression prediction algorithms are relatively high on account of the complexity of the cerebral time series. Hence we set the seizure forecast problem as a time series classification(TSC) problem and a cutting-edge TSC algorithm:the Hierarchical Vote Collective of Transformation-based Ensembles(HIVE-COTE) is introduced to address the issue at the first time. In addition, we improve the structure of HIVE-COTE using a channel selection module. With the improved HIVE-COTE model, an oncoming seizure could be detected by a collective of machine-learning classifiers that identify the accurate pattern(inter-ictal or pre-ictal) of the cerebral signals. Based on 15 patients in CHB-MIT dataset, the proposed improved HIVE-COTE model achieves a sensitivity of 87.4%, an AUC of 0.859, which is a promising result. Our work significantly outperform a random predictor and other TSC algorithms.