Cloud EEG Privacy Using Red-Billed Blue Magpie Optimized Physics-Penalized Dual-Branch Spectral-Spatial Neural Network for Epileptic Seizure Prediction.

Abstract

Epileptic seizure prediction is a critical research area that enables timely intervention and prevention of severe neurological complications. With the growing integration of IoT in healthcare, real-time EEG monitoring has become essential for continuous and automated seizure detection. The suggested approach presents a hybrid deep learning architecture that integrates various sophisticated computational approaches to deliver precise, safe, and effective seizure prediction. EEG data are recorded in real time with an IoT-based headband and processed with Shape-Aware Mesh Normal Filtering (SMNF) in order to eliminate noise and enhance the quality of the signal. In addition to that, the Quadratic Phase Quaternion Domain Fourier Transform (QPQDFT) is the feature extraction principle that is effective in both spectral and temporal variations. The features extracted are then categorized with Physics-Penalized Dual-Branch Spectral-Spatial Neural Network (PP-DBSSNN), which employs physics-based regularization and dual-branch attention as a way of enhancing generalization and interpretability of the data. Finally, Key Escrow-Free Attribute-Based Encryption (KEF-ABE) is a method that guarantees the security and privacy of EEG information on clouds. The findings of the experiment show the best performance with an accuracy of 99.95%, a precision of 99.93%, and a specificity of 99.91% in the case of the Bonn EEG dataset, and an accuracy of 99.96%, a precision of 99.94%, and a specificity of 99.92% in the case of the CHB-MIT dataset, which confirms its robustness and reliability.