A patient-specific seizure prediction algorithm is proposed that extracts novel multivariate signal coherence features from ECoG recordings and classifies a patient's pre-seizure state. The algorithm uses space-delay correlation and covariance matrices at several delay scales to extract the spatiotemporal correlation structure from multichannel ECoG signals. Eigenspectra and amplitude features are extracted from the correlation and covariance matrices, followed by dimensionality reduction using principal components analysis, classification using a support vector machine, and temporal integration to produce a seizure prediction score. Evaluation on the Freiburg EEG database produced a sensitivity of 90.8% and false positive rate of .094.
['James R. Williamson', 'Daniel W. Bliss', 'David W. Browne']
Epileptic seizure prediction using the spatiotemporal correlation structure of intracranial EEG