  Objective: The aim of this study is to develop an efficient and reliable
epileptic seizure prediction system using intracranial EEG (iEEG) data,
especially for people with drug-resistant epilepsy. The prediction procedure
should yield accurate results in a fast enough fashion to alert patients of
impending seizures. Methods: We quantitatively analyze the human iEEG data to
obtain insights into how the human brain behaves before and between epileptic
seizures. We then introduce an efficient pre-processing method for reducing the
data size and converting the time-series iEEG data into an image-like format
that can be used as inputs to convolutional neural networks (CNNs). Further, we
propose a seizure prediction algorithm that uses cooperative multi-scale CNNs
for automatic feature learning of iEEG data. Results: 1) iEEG channels contain
complementary information and excluding individual channels is not advisable to
retain the spatial information needed for accurate prediction of epileptic
seizures. 2) The traditional PCA is not a reliable method for iEEG data
reduction in seizure prediction. 3) Hand-crafted iEEG features may not be
suitable for reliable seizure prediction performance as the iEEG data varies
between patients and over time for the same patient. 4) Seizure prediction
results show that our algorithm outperforms existing methods by achieving an
average sensitivity of 87.85% and AUC score of 0.84. Conclusion: Understanding
how the human brain behaves before seizure attacks and far from them
facilitates better designs of epileptic seizure predictors. Significance:
Accurate seizure prediction algorithms can warn patients about the next seizure
attack so they could avoid dangerous activities. Medications could then be
administered to abort the impending seizure and minimize the risk of injury.
