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This study aims to improve the accuracy of epileptic seizure prediction using cloud-based, real-time electroencephalogram analysis. The goal is to build a strong framework that can quickly process electroencephalogram (EEG) data, extract relevant features, and use advanced machine learning algorithms to predict seizures with high accuracy and low latency by taking advantage of cloud platforms' computing power and scalability. The main objective is to provide patients and their caregivers with timely notifications so that they may control epilepsy episodes proactively. The goal of this project is to improve the lives of people with epilepsy by reducing the impact of seizures and improving treatment results via real-time analysis of EEG data. Cloud computing also allows the suggested seizure prediction system to be more accessible and scalable, meaning more people worldwide could benefit from it. This section discusses the results from five separate datasets of patients with epileptic seizures who underwent EEG analysis with the following details as frontopolar (FP1, FP2), frontal (F3, F4), frontotemporal (F7, F8), central (C3, C4), temporal (T3, T4), parieto-temporal (T5, T6), parietal (P3, P4), occipital (O1, O2), time (HH:MM:SS).
Thahniyath et al. (Fri,) studied this question.