Deep Seizure: An Enhanced System for Detecting Epileptic Seizure in Real-Time Based on EEG Signals Utilizing Sliding Window-Based Deep Learning Approach

Research explores epilepsy detection in real-time scenarios using a deep-learning framework with an innovative sliding window design approach. The primary challenge is that it is quite difficult to detect epileptic seizures in real-time using EEG signals accurately. Traditional methods are time-consuming and depend upon manual analysis of EEG data. In addition, variability in the seizure patterns and the presence of noise in EEG recordings can be complicated during the classification process. To overcome the problem, we will proceed with the following: The EEG signals will be taken as input, and with the help of CNN, the classification between seizure and non-seizure patients will be differentiated. The framework enhances the CNN implementation on the EEG recordings to evaluate the activity. The sliding window design will be helpful to make sure that the classes will be balanced within the training samples for improved accuracy. The outcome will be the enhancement of classification accuracy, and clinical decision-making, and the potential for future advancements in epilepsy management.