Approximately 1% of people in the world have epilepsy and 25% of epilepsy patients cannot be treated sufficiently by any available therapy. An automatic seizure detection system can reduce the time taken to review the EEG data by the neurologist for epilepsy diagnosis. In this paper, various EEG features integrated with the linear or non-linear classifiers are evaluated for seizure detection. For the EEG features, approximate entropy (ApEn) combined with 1) EEG power spectra or 2) autoregressive model (AR) are compared. In addition, the principle component analysis (PCA) is also utilized for feature extraction. For the classifiers, two linear models, linear least square (LLS) and linear discriminant analysis (LDA), and two nonlinear models, backpropagation neural network (BPNN) and support vector machine with radial basis function kernel (RBFSVM) are compared. The EEG signals of three Long Evans rats with spontaneous absence seizures are used for leave-one-out cross-validation. Experimental results shows that combining ApEn and multi-band EEG power spectra are superior to the combination of ApEn and AR model for all classifiers. The best average accuracy is 97.5% performed by RBFSVM and the linear models can achieve to higher than 95%. The automatic seizure detection method can be utilized to drive the seizure warning device or seizure control devices in the future to enhance the patients' quality of life.