Neural Network based Ramp Raise Optimized Algorithm for Prediction of Seizure Pre-ictal Stage

People with epilepsy will benefit from the adoption of the Brain Computer Interface (BCI) for seizure prediction. Epilepsy is a frequent neurological condition of the brain's Central Nervous System that has negative consequences for patients. The human brain has millions of neurons, each of which is responsible for a distinct motor action in the human body. The neurons send out aberrant electrical signals that go throughout the body, causing involuntary movements and seizures. During the seizure, the patient collapses and experiences jerky movements, tongue biting, nausea, and other symptoms. Recurrent seizures are a hallmark of epilepsy patients. EEG (Electroencephalogram) is one of the common procedures to observe the signals produced by brain and helps to examine seizure occurrence in the patients. Seizure has four different stages ictal (during seizure), pre-ictal (before seizure), inter ictal (time interval between two successive seizures) and post ictal (after seizure) (after seizure). The patient will be kept in a safer state if the pre-ictal stage of the patient can be predicted. With the introduction of cutting-edge Neural Network techniques, their various activation functions, and Gradient Boosting, the patient's pre-ictal state can be predicted and diagnosed, allowing proper seizure medication to be administered. The suggested model uses the Neural Network Based Ramp Raise Optimized Algorithm (NNBRROA) with hyper parameter tuning to predict pre-ictal patients with 98 percent accuracy.