Seizure Prediction by Graph Mining, Transfer Learning, and Transformation Learning

We present in this study a novel approach to predicting EEG epileptic seizures: we accurately model and predict non-ictal cortical activity and use prediction errors as parameters that significantly distinguish ictal from non-ictal activity. We suppress seizure-related activity by modeling EEG signal acquisition as a cocktail party problem and obtaining seizure-related activity using Independent Component Analysis. Following recent studies intricately linking seizure to increased, widespread synchrony, we construct dynamic EEG synchronization graphs in which the electrodes are represented as nodes and the pair-wise correspondences between them are represented by edges. We extract 38 intuitive features from the synchronization graph as well as the original signal. From this, we use a rigorous method of feature selection to determine minimally redundant features that can describe the non-ictal EEG signal maximally. We learn a one-step forecast operator restricted to just these features, using autoregression AR1. We improve this in a novel way by cross-learning common knowledge across patients and recordings using Transfer Learning, and devise a novel transformation to increase the efficiency of transfer learning. We declare imminent seizure based on detecting outliers in our prediction errors using a simple and intuitive method. Our median seizure detection time is 11.04﾿min prior to the labeled start of the seizure compared to a benchmark of 1.25﾿min prior, based on previous work on the topic. To the authors' best knowledge this is the first attempt to model seizure prediction in this manner, employing efficient seizure suppression, the use of synchronization graphs and transfer learning, among other novel applications.