Annalisa Dellavalle
Microstructure-informed deep learning for sleep disorder classification using SO–Spindle dynamics.
Rel. Valentina Agostini, Francesca Dalia Faraci, Luigi Fiorillo. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2026
Abstract
Sleep disorders affect a substantial proportion of the global population and are associated with cardiovascular, metabolic, and neurodegenerative comorbidities. In clinical practice, gold-standard diagnosis relies on overnight polysomnography (PSG), a multivariate and multimodal recording of physiological signals, typically reduced to discrete 30-seconds sleep stages, thereby discarding the continuous oscillatory dynamics underlying brain sleep microstructure. In particular, phase-amplitude coupling (PAC) between slow oscillations (SOs, i.e., characteristic of deep non-REM sleep) and sleep spindles (i.e., transient faster thalamocortical bursts) may represent a stable, individual electroencephalographic (EEG) fingerprint, which is systematically distorted in pathological conditions and may provide biologically grounded basis for automated classification of sleep-wake disorders.
This thesis aims to develop and validate an automated classification pipeline based on SO–spindle microstructural dynamics, focusing on sleep-related breathing disorders within a single-centre clinical cohort, while establishing the methodological foundation for future multi-centre extensions
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