A Distributed Descriptor Characterizing Structural Irregularity of EEG Time Series for Epileptic Seizure Detection

This paper presents a novel descriptor aiming at anomaly detection in sequential data, like epileptic seizure detection with EEG time series. The descriptor is derived from the eigenvalue decomposition (EVD) of a Hankel-form data matrix generated from the raw time series. Simulation trials imply that the descriptor is capable of characterizing the structural aspect of a time series. In addition, we deploy the proposed descriptor as a feature extractor and apply it on Bonn Seizure Database which is widely used in seizure detection. The high accuracies on classification problems are comparable with the state-of-the-art so validate the effectiveness of our method.