Quantum inspired wavelet and Fourier feature fusion for EEG based epilepsy and seizure detection.

The electroencephalography (EEG) signals are the cheapest approach to study the brain information, commonly used for epilepsy and seizure detection. This study presents a QFF-ML Net (Quantum Feature Fused Machine Learning Network). This novel approach integrates Quantum Wavelet Transform (QWT) and Quantum Fourier Transform (QFT) with a dimensionality reduction method. The raw EEG signals are not directly transmitted for remote monitoring; signal preprocessing and feature extraction are done using PCA applied dataset with QWT and QFT, then the quantum encrypted file is transferred via Ultra-reliable low-latency communication (uRLLC), which facilitates real-time remote diagnosis. This approach effectively reduces computational complexity while preserving critical seizure-related features, making it well-suited for bandwidth-constrained environments. Experimental findings show that QFF-ML Net achieved high performance across datasets, with accuracies of 92.3% on Bonn, 91.0% on CHB-MIT, and 88.3% on real-time 5G remote data, maintaining strong precision, recall, and F1-scores in all cases in EEG-based epilepsy and seizure detection on 5G-enabled remote healthcare networks.