Seizure Detection based on Spatiotemporal Correlation and Frequency Regularity of Scalp EEG

In this paper, a robust seizure detection system using scalp EEG signal is presented. Two most important and obvious characteristics of seizure EEG, signal variance, and frequency synchronization are carefully chosen as seizure detection indexes. To extract the representation of EEG variance, a spatiotemporal correlation structure is constructed based on space-delay covariance matrices with multi-scale temporal delay. The frequency synchronization of EEG is represented by a regularity index derived from wavelet packet transform. The extracted representations are combined to form a high-dimensional feature vector with redundant information. In order to reduce the redundancy, feature selection is performed using mutual information (MI) based on best individual features. The optimized set of features form a more compact feature vector for each 2-s epoch of multi-channel EEG. Feature vectors are then classified into ictal or interictal class using a linear support vector machine (SVM). To evaluate the proposed seizure detection system, unbiased leave-one-session-out cross-validation using clinical routine EEG from 7 patients are performed in experiments. The proposed method obtains average accuracy of 91.44% and average latency of 6.82 s, which outperforms other 7 commonly used methods. It is also demonstrated that the performance of our method is more robust since the standard deviation of results among patients is smaller than other methods.