Detecting Epileptic Seizures from EEG Data using Neural Networks
  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. 
Siddharth Pramod, Adam Page, Tinoosh Mohsenin and Tim Oates
cs.LG cs.NE q-bio.NC