  Objective: Forecasting epileptic seizures can reduce uncertainty for patients
and allow preventative actions. While many models can predict the occurrence of
seizures from features of the EEG, few models incorporate changes in features
over time. Long Short-Term Memory (LSTM) neural networks are a machine learning
architecture that can display temporal dynamics due to the recurrent
connections. In this paper, we used LSTMs to monitor changes in EEG features
over time to improve the accuracy of seizure forecasts and to alter the time
window of the forecast. Methods: Long-term intracranial EEG recordings from
eight patients from the NeuroVista dataset were used. A Fourier transform of
1-minute segments of EEG was fed into a Convolutional Neural Network (CNN). The
outputs from the CNN were input to three different LSTM models at different
time intervals: 1 minute, 1 hour and 1 day. The LSTM model outputs were used to
predict seizure onset within a time window. The prediction and start of the
time window were separated by the same length of time as the window. Window
sizes tested included 2, 4, 10, 20 and 40 minutes. Results and Conclusion: Our
model forecast seizure onsets well above a random predictor. Compared to other
models using the same dataset, our model performed better for some patients and
worse for others. Monitoring the change in EEG features over time allowed our
model to produce good results over a range of different window sizes, which is
an improvement on previous models and raises the possibility of altering the
forecast to meet individual patient needs. Furthermore, a window size of 40
minutes provides a potential intervention time of 40 minutes, which is the
first time an intervention time of more than 5 minutes have been forecast using
long-term EEG recordings.
