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Seizure prediction has attracted a growing attention as one of the mostchallenging predictive data analysis efforts in order to improve the life ofpatients living with drug-resistant epilepsy and tonic seizures. Manyoutstanding works have been reporting great results in providing a sensibleindirect (warning systems) or direct (interactive neural-stimulation) controlover refractory seizures, some of which achieved high performance. However,many works put heavily handcraft feature extraction and/or carefully tailoredfeature engineering to each patient to achieve very high sensitivity and lowfalse prediction rate for a particular dataset. This limits the benefit oftheir approaches if a different dataset is used. In this paper we applyConvolutional Neural Networks (CNNs) on different intracranial and scalpelectroencephalogram (EEG) datasets and proposed a generalized retrospectiveand patient-specific seizure prediction method. We use Short-Time FourierTransform (STFT) on 30-second EEG windows with 50% overlapping to extractinformation in both frequency and time domains. A standardization step is thenapplied on STFT components across the whole frequency range to prevent highfrequencies features being influenced by those at lower frequencies. Aconvolutional neural network model is used for both feature extraction andclassification to separate preictal segments from interictal ones. The proposedapproach 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 KaggleAmerican Epilepsy Society Seizure Prediction Challenge's dataset, respectively.Our prediction method is also statistically better than an unspecific randompredictor for most of patients in all three datasets.

A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis