A Theoretical Proposal: Utilizing Quantum Walks for Feature Extraction in PSG-Based Sleep Stage Classification

Abstract

This work proposes a novel theoretical framework utilizing quantum walk principles for feature extraction from polysomnographic (PSG) signals, focusing on the classification of sleep stages. By mapping EEG signals to a quantum graph and leveraging the probabilistic and path-dependent properties of quantum walks, this approach offers a potentially transformative methodology for analyzing brain activity. Also discussing the potential solution to class imbalance in sleep datasets using the innate qualities of quantum mechanics like superposition, highlighting the possible use of a hybrid loss function for classification.Outlining the conceptual basis of this framework, discussing its implications, and suggesting potential applications and future research directions.