Neurofusionnet: a comprehensive framework for accurate epileptic seizure prediction from EEG data with hybrid meta-heuristic optimization algorithm.

Abstract

This work uses cutting edge Electroencephalogram (EEG) data processing techniques to present a complete paradigm for epileptic seizure prediction. The methodology is a multi-step procedure that includes pre-processing, feature extraction, feature selection, and a new detection model based on deep learning for enhanced durability and accuracy. Bandpass filtering is used to reduce noise during the pre-processing phase, which improves the signal-to-noise ratio. EEG data quality is further improved using Independent Component Analysis, which finds and removes artifacts. Splitting continuous EEG data into fixed-duration segments, known as epoching, facilitates the investigation of discrete temporal patterns. Standard amplitude values are guaranteed by Z-score normalization, and seizure-related patterns are more sensitively detected when channels are selected using Common Spatial Patterns. Step one of the feature extraction processes involves statistical features and time-domain features. For spectrum information it is essential to recognizing seizures, frequency-domain features such as Power spectrum Density are extracted using a technique Fourier Transform. A full representation is obtained by extracting Time-Frequency Domain Features with the Wavelet Transform. Predictive power is increased by the efficient selection of discriminative characteristics through the use of a hybrid optimization model called Hybrid Chimp Enhanced Fox Optimization algorithm that combines optimization methods inspired by FOX and Chimp. The suggested NeuroFusionNet-based detection model combines Improved ShuffleNet V2, SqueezeNet, EfficientNet V2, and Multi Head Attention (MHA) based GhostNet V2, which captures complex patterns linked to epileptic episodes.