  Approximately over 50 million people worldwide suffer from epilepsy.
Traditional diagnosis of epilepsy relies on tedious visual screening by highly
trained clinicians from lengthy EEG recording that contains the presence of
seizure (ictal) activities. Nowadays, there are many automatic systems that can
recognize seizure-related EEG signals to help the diagnosis. However, it is
very costly and inconvenient to obtain long-term EEG data with seizure
activities, especially in areas short of medical resources. We demonstrate in
this paper that we can use the interictal scalp EEG data, which is much easier
to collect than the ictal data, to automatically diagnose whether a person is
epileptic. In our automated EEG recognition system, we extract three classes of
features from the EEG data and build Probabilistic Neural Networks (PNNs) fed
with these features. We optimize the feature extraction parameters and combine
these PNNs through a voting mechanism. As a result, our system achieves an
impressive 94.07% accuracy, which is very close to reported human recognition
accuracy by experienced medical professionals.
