Greta Furlotti
Obstructive sleep apnea detection: exploring neural activity variations through statistical analysis and machine learning.
Rel. Valentina Agostini, Francesca Dalia Faraci. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2023
Abstract
Sleep disorders are a widespread health condition. Around 40% of adults in the United States experience sleep-related breathing issues, including obstructive sleep apnea (OSA). OSA consists in a reduction or interruption of airflow, due to airway obstruction. It is associated with various clinical conditions, health issues and alterations in the sleep electroencephalogram (EEG). Polysomnography (PSG), a medical overnight test that monitors various physiological activities during sleep, is the basis for OSA diagnosis. EEG is an essential component of PSG, used for classification of sleep stages and neural event detection, including arousals. The primary hypothesis of this thesis aimed to investigate differences in neural activity between healthy and OSA subjects.
This research involved exploring variations in neural function by analysing the relative power bands of Power Spectral Density (PSD) extracted from EEG signal
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