Augmenting Data from Epileptic Brain Seizures Using Deep Generative Networks

In many domains including medicine, biology, and neuroscience, rare events are the norm rather than the exception, limiting the ability to train intelligent systems to perform reliable pattern classification. This is the case when monitoring brain activity for epileptic seizures that constitute infrequent periods when abnormal electrical activity propagates across clusters of neurons. Here, as a solution, we describe how a generative adversarial network (GAN) can serve to produce synthetic examples that capture key features of epileptic activity observed in networks of in vitro cortical neurons. Further, GANs can generate novel patterns that deviate in systematic ways from the original data. A convolutional neural network whose goal was to classify healthy and seizure activity attained higher performance when trained on an augmented dataset composed of both original and synthetic data. Altogether, this work shows how GANs can provide data augmentation in a domain of epileptic seizures characterized by rare events.