n this paper, we propose an efficient seizure prediction model based on a two-layer LSTM with the Swish activation function. The proposed structure performs feature extraction based on the time and frequency domains and uses the minimum distance algorithm as a post-processing step. The proposed model is evaluated on the Melbourne dataset and achieves the highest Area Under Curve (AUC) of 0.917 and the lowest False Positive Rate (FPR) of 0.12 compared to previous work while having sensitivity and accuracy of 84.7 and 84.8, respectively. The proposed system has a low number of trainable parameters and thus can enable on-chip training in resource-constrained applications, such as wearable devices.