Reliable prediction of forthcoming seizures will be a milestone in epilepsy research. A method capable of timely predicting the occurrence of seizures could significantly improve the quality of life for epilepsy patients and open new therapeutic approaches. Seizures are usually characterized by generalized spike wave discharges. With the advent of seizures, the variation of spike rate (SR) will have different manifestations. In this study, a seizure prediction approach based on spike rate is proposed and evaluated. Firstly, a low-pass filter is applied to remove the high frequency artifacts in electroencephalogram (EEG). Then, the morphology filter is used to detect spikes and compute SR, and SR is smoothed with an average filter. Finally, the performance of smoothed SR (SRm) in EEG during interictal, preictal, and ictal periods is analyzed and employed as an index for seizure prediction. Experiments with long-term intracranial EEGs of 21 patients show that the proposed seizure prediction approach achieves a sensitivity of 75.8% with an average false prediction rate of 0.09/h. The low computational complexity of the proposed approach enables its possibility of applications in an implantable device for epilepsy therapy.