Long-term　epileptic　seizure　prediction　has　potential　to　transform　epilepsy　care　and　treatment.　However,　the　accuracy　of　seizure　prediction　is　still　difficult　to　satisfy　the　requirement　of　application.　In　this　paper,　a　seizure　prediction　system　is　proposed　based　on　Bag-of-Wave　Model　and　Extreme　Learning　Machine.　To　get　the　representation　of　segments　in　iEEG　signals,　interictal　codebook　and　preictal　codebook　are　constructed　by　clustering　algorithm.　Histogram　features　are　then　extracted　by　projecting　waves　within　the　sliding　window　on　two　codebooks.　In　the　end,　classifying　the　feature　with　ELM　into　interictal　phase　and　preictal　phase.　Experiments　are　operated　on　Kaggle　Seizure　Prediction　Challenge　dataset,　which　show　the　proposed　approach　is　effective　in　seizure　prediction.