  Approximately, 50 million people in the world are affected by epilepsy. For
patients, the anti-epileptic drugs are not always useful and these drugs may
have undesired side effects on a patient's health. If the seizure is predicted
the patients will have enough time to take preventive measures. The purpose of
this work is to investigate the application of bidirectional LSTM for seizure
prediction. In this paper, we trained EEG data from canines on a double
Bidirectional LSTM layer followed by a fully connected layer. The data was
provided in the form of a Kaggle competition by American Epilepsy Society. The
main task was to classify the interictal and preictal EEG clips. Using this
model, we obtained an AUC of 0.84 on the test dataset. Which shows that our
classifier's performance is above chance level on unseen data. The comparison
with the previous work shows that the use of bidirectional LSTM networks can
achieve significantly better results than SVM and GRU networks.
