Accurate and timely detection of epileptic seizures is critical for clinical intervention however, conventional EEG analysis is constrained by noise, inter-patient variability, and high computational demands. To overcome this the proposed lightweight Hybrid Quantum classical Ensemble Net (HQCE-Net) optimized for edge device. The proposed pipeline with filtering, noice reduction scaling to suppress artifacts, followed by extraction of features on EEG sub-band powers (\(\delta\), \(\theta\), \(\alpha\), \(\beta\)) and MRI images multi model feature fusion using Quantum Fourier Transform (QFT) and Quantum Wavelet Transform (QWT) enabling complementary frequency-domain and multi-resolution time–frequency representation while maintaining computational efficiency. The fused features are standardized using z-score normalization. The complete system is implemented on a Raspberry Pi 5, On the EEG dataset (CHBMIT), HQCE-Net achieves about 92% accuracy with 92% performance metrics. On the MRI dataset it achieves 98% accuracy with 98% precision/recall, showing strong and consistent performance across both EEG and MRI modalities on edge device. Unlike existing EEG seizure detection methods that rely on computationally intensive deep models or cloud-based processing, This work demonstrates the practical and reliable neurodiagnostic monitoring for home-based and resource-limited clinical environments.
