Generalizable Seizure Detection Model Using Generating Transferable Adversarial Features

Abstract

Epilepsy is the second most common neurological disorder, affecting 65 million people around the world. It is associated with seizures - a sudden, uncontrolled electrical disturbance in the brain that can lead to profound transient changes in behavior, movements, feelings, and levels of consciousness. Current approaches to developing a generalized automated seizure detection algorithm rely on constructing large, labeled training and test corpora of electroencephalograms (EEGs) from different individuals. However, due to the inherent inter-subject variability, heterogeneity of acquisition hardware, different montage choices, and various recording environments, EEG patterns may exhibit very different distributions over time and between individuals. Therefore, training an algorithm on such data without accounting for this diversity can affect the performance of any classifier or predictor. In addition, this process smooths out individual differences, producing a general, but a non-specific model. To address these issues, we propose a novel method that generates transferable features to interpolate between features from the training and test sets. This is achieved by adversarially training deep classifiers to make consistent classifications or predictions over the transferable features. Experiments on an EEG seizure databases demonstrate that the proposed method increases the accuracy over state-of-the-art from 86.83% to 91.71% and specificity from 87.38% to 94.73% while reducing the false positive rate/hour from 0.8/hour to 0.58/hour. Therefore, this work has the potential for significantly reducing workload in reviewing clinical EEGs for seizures, and for improved real-time closed-loop vagal stimulation.