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Epilepsy is a neurological condition that distresses millions of individuals throughout the world. The lives of epileptic patients are greatly influenced by the early detection of impending seizures. In this paper, Design an Efficient FPGA Based Hardware Implementation for Real-Time Mobile Epileptic Seizure Prediction Using Deep Neural Network. This research study uses correlation deep learning replicas through an FPGA enactment of the modified short-time Fourier transform (MSTFT) block to improve epileptic seizure detection. EEG data is pre-processed for time-frequency analysis of EEG segments using an FPGA-based MSTFT. Mounting-based Matrix Factorization (MMF) extract frequency bands and feature space. Seizure detection using proposed Correlation-based deep learning neural Network (CDNN) for classification. Each layers hybrid using hyperparameter optimized using Entropy-based Grey Wolf Optimizer (EGWO) algorithm. The experimental outcomes will demonstrate that our presented technique passes the traditional techniques.

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