  Seizure prediction has attracted a growing attention as one of the most
challenging predictive data analysis efforts in order to improve the life of
patients living with drug-resistant epilepsy and tonic seizures. Many
outstanding works have been reporting great results in providing a sensible
indirect (warning systems) or direct (interactive neural-stimulation) control
over refractory seizures, some of which achieved high performance. However,
many works put heavily handcraft feature extraction and/or carefully tailored
feature engineering to each patient to achieve very high sensitivity and low
false prediction rate for a particular dataset. This limits the benefit of
their approaches if a different dataset is used. In this paper we apply
Convolutional Neural Networks (CNNs) on different intracranial and scalp
electroencephalogram (EEG) datasets and proposed a generalized retrospective
and patient-specific seizure prediction method. We use Short-Time Fourier
Transform (STFT) on 30-second EEG windows with 50% overlapping to extract
information in both frequency and time domains. A standardization step is then
applied on STFT components across the whole frequency range to prevent high
frequencies features being influenced by those at lower frequencies. A
convolutional neural network model is used for both feature extraction and
classification to separate preictal segments from interictal ones. The proposed
approach achieves sensitivity of 81.4%, 81.2%, 82.3% and false prediction rate
(FPR) of 0.06/h, 0.16/h, 0.22/h on Freiburg Hospital intracranial EEG (iEEG)
dataset, Children's Hospital of Boston-MIT scalp EEG (sEEG) dataset, and Kaggle
American Epilepsy Society Seizure Prediction Challenge's dataset, respectively.
Our prediction method is also statistically better than an unspecific random
predictor for most of patients in all three datasets.
