The electroencephalogram (EEG) has proved to be a valuable tool in the study of comprehensive conditions whose effects are manifest in the electrical brain activity, and epilepsy is one of such conditions. In the study, multiscale permutation entropy (MPE) was proposed to describe dynamical characteristics of EEG recordings from epilepsy and healthy subjects, then all the characteristic parameters were forwarded into a support vector machine (SVM) for classification. The classification accuracies of the MPE with SVM were evaluated by a series of experiments. It is indicated that the dynamical characteristics of EEG data with MPE could identify the differences among healthy, interictal and ictal states, and there was a reduction of MPE of EEG from the healthy and interictal state to the ictal state. Experimental results demonstrated that average classification accuracy was 100% by using the MPE as a feature to characterize the healthy and seizure, while 99. 58% accuracy was obtained to distinguish the seizure-free and seizure EEG. In addition, the single-scale permutation entropy (PE) at scales 1-5 was put into the SVM for classification at the same time for comparative analysis. The simulation results demonstrated that the proposed method could be a very powerful algorithm for seizure prediction and could have much better performance than the methods hased on single sale PF