A novel framework based on biclustering for automatic epileptic seizure detection

Abstract

Automatic epileptic seizure detection based on electroencephalogram is crucial to epilepsy diagnosis and treatment. However, the large numbers of time series make it quite challenging to establish a high performance automatic detection method. Considering different physiological states of the brain could be characterized by distinct combinations or interactions of similar discontinuous local temporal patterns, a novel framework based on biclustering for automatic epileptic seizure detection is proposed in this paper. First, the CC algorithm is used to identify similar discontinuous local temporal patterns. Then, the bicluster membership matrix using a new similarity measurement is constructed to reduce the dimensionality. At last, the ELM classifier is adopted to discriminate between epileptic seizure and seizure-free EEG signals. With extensive comparative studies and evaluations on the publicly available Bonn epileptic EEG dataset, it indicates that the proposed framework could not only automatically detect or predict an epilepsy seizure with high performances with respect to accuracy, robustness and efficiency, but also implicitly provide valuable knowledge for studying the mechanisms of epilepsy.