  Epilepsy is a chronic neurological disorder affecting more than 65 million
people worldwide and manifested by recurrent unprovoked seizures. The
unpredictability of seizures not only degrades the quality of life of the
patients, but it can also be life-threatening. Modern systems monitoring
electroencephalography (EEG) signals are being currently developed with the
view to detect epileptic seizures in order to alert caregivers and reduce the
impact of seizures on patients' quality of life. Such seizure detection systems
employ state-of-the-art machine learning algorithms that require a considerably
large amount of labeled personal data for training. However, acquiring EEG
signals of epileptic seizures is a costly and time-consuming process for
medical experts and patients, currently requiring in-hospital recordings in
specialized units. In this work, we generate synthetic seizure-like brain
electrical activities, i.e., EEG signals, that can be used to train seizure
detection algorithms, alleviating the need for recorded data. First, we train a
Generative Adversarial Network (GAN) with data from 30 epilepsy patients. Then,
we generate synthetic personalized training sets for new, unseen patients,
which overall yield higher detection performance than the real-data training
sets. We demonstrate our results using the datasets from the EPILEPSIAE
Project, one of the world's largest public databases for seizure detection.
