Epileptic　seizure　prediction　has　the　potential　to　promote　epilepsy　care　and　treatment.　However,　the　seizure　prediction　accuracy　does　not　satisfy　the　application　requirements.　In　this　paper,　a　novel　framework　for　seizure　prediction　is　proposed　by　learning　synchronization　patterns.　For　better　representation,　bag-of-wave　(BoWav)　feature　extraction　is　proposed　for　modeling　synchronization　pattern　of　electroencephalogram　(EEG)　signal.　An　interictal　codebook　and　preictal　codebook,　representing　the　local　segments,　are　constructed　by　a　clustering　algorithm.　Within　a　period　of　EEG　signal　on　all　electrodes,　local　segments　are　projected　onto　the　learned　codebooks.　The　proposed　feature　expresses　the　synchronization　pattern　of　EEG　signal　with　the　histogram　feature.　Moreover,　extreme　learning　machine　(ELM)　is　used　to　classify　the　sequence　of　features.　Experiments　are　performed　on　the　Kaggle　seizure　prediction　challenge　dataset　and　the　CHB-MIT　dataset.　The　experiment　on　the　CHB-MIT　achieves　a　sensitivity　of　88.24%　and　a　false　prediction　rate　per　hour　of　0.25.　©　2018,　Springer-Verlag　GmbH　Germany,　part　of　Springer　Nature.