Exploring Quantum Recurrent Neural Networks in Sleep Medicine
Year: 2025
Type: article
Abstract: Sleep medicine is devoted to the diagnosis and therapy of different sleep disorders with potential serious impacts on the health condition of individuals. The reference test to assess many of these disorders is the polysomnography (PSG). Clinical analysis of the PSG recording is expensive, requires substantial time and effort, and is prone to unintentional human misinterpretations, which is why automation of this process has been extensively studied throughout the last years. The complexity of the task, on the other hand, offers an ideal scenario for the benchmarking of new developments in the field of machine learning with potential contributions to the task. This work examines the applicability of quantum machine learning for the diagnosis of sleep-disordered breathing, a common sleep disorder related to the repeated occurrence of involuntary respiratory pauses during sleep. By computing the state evolution through density matrices, we emulate the behavior of a quantum recurrent neural network to study the feasibility of automatic classification of PSG sequences with the presence or absence of respiratory events (apnea/hypopnea)
Authors: Viqueira Cao, José Daniel, Mayorga-Redondo, Alejandro, Magaz Romero, Samuel, Mosqueira Rey, Eduardo, Gómez Tato, Andrés, Alvarez-Estevez, Diego
Institutions: Centro de Supercomputación de Galicia, Universidade da Coruña
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7108229753