Un modello di apprendimento profondo end-to-end per la classificazione automatica degli stadi del sonno utilizzando segnali fisiologici minimi = An End-to-End Deep Learning Model for Automatic Sleep Stage Classification Using Minimal Physiological Signals
Baharak Qaderi
Un modello di apprendimento profondo end-to-end per la classificazione automatica degli stadi del sonno utilizzando segnali fisiologici minimi = An End-to-End Deep Learning Model for Automatic Sleep Stage Classification Using Minimal Physiological Signals.
Rel. Luigi Borzi', Irene Rechichi. Politecnico di Torino, Corso di laurea magistrale in Data Science And Engineering, 2025
Abstract
Sleep plays a key role in overall health and well-being, which makes it a significant topic for academic investigation. Polysomnography (PSG) is commonly used by researchers to analyse sleep, as it offers detailed insights via multiple physiological signals. However, PSG is expensive, technically complex, and typically confined to clinical environments for a single night. To enhance accessibility and enable long-term monitoring, data from commercially available wearable sensors can be utilized. Yet, for these sensors to reliably detect and interpret sleep patterns or diagnose sleep disorders robust algorithms are required. Such algorithms must accurately analyse sensor-derived signals to classify or predict various sleep stages.
This study proposes an end-to-end deep learning model for automatic sleep stage classification using minimal physiological signals
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