Hybrid quantum classical framework for electroencephalogram driven neurological processing in epileptic seizure taxonomy.

Epileptic seizures are significant challenges in a neurological environment primarily due to the non-stationary and complex nature of the electroencephalogram (EEG) signals. In this paper, we present a Hybrid Quantum-Classical Neural Framework (HQCNF) that leverages quantum computing to support taxonomy of epilepsy seizures with deep neural learning. The framework applies a Continuous Wavelet Transform (CWT) to convert EEG recordings into a time-frequency representation deploying scalograms intended to rigorously scrutinize the features describing oscillations and other elements noted when seizures occur. The model presented in this paper is built from classical architecture and uses quantum-inspired neural layers to support providing the atomic feature representations in order to make sense of discriminability behavior and other elements of interpretable behavior in epistemology and learning. The HQCNF model achieves 99% classification accuracy and provides evidence of enhanced generalization, and outperforms typical deep learning models. The research supports the methodology of hybrid quantum-classical paradigms to move beyond a conventional computing biomedical signal analysis restriction, and the ability to examine infrastructure presented by HQCNF moves us toward real-time assessments and promotes further examination of efforts towards intelligent diagnostic methodologies utilized in neurological disorder management frameworks. The work focuses on the value of quantum-enhanced learning as it relates to EEG-based examination in epilepsy management, and makes advanced hybrid-learning implementations of quantum-enhanced learning that provides for more efficient and reliable accuracy in EEG-based examination of epilepsy signal processing.