  We explore the use of neural networks trained with dropout in predicting
epileptic seizures from electroencephalographic data (scalp EEG). The input to
the neural network is a 126 feature vector containing 9 features for each of
the 14 EEG channels obtained over 1-second, non-overlapping windows. The models
in our experiments achieved high sensitivity and specificity on patient records
not used in the training process. This is demonstrated using
leave-one-out-cross-validation across patient records, where we hold out one
patient's record as the test set and use all other patients' records for
training; repeating this procedure for all patients in the database.
