Nicolo' La Porta
Sleep apnea events recognition based on polysomnographic signals recordings: a machine learning approach.
Rel. Filippo Molinari. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2022
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Abstract
Sleep diseases are one of the major causes of physical and psychological problems for workers, resulting in high financial losses in terms of direct, indirect, related and intangible costs. This thesis will focus on a particular type of sleep-related disorder, the Sleep Apnea-Hypopnea Syndrome (SAHS) which causes numerous involuntary respiratory pauses during the night (“apneic events”) leading to a drop of blood oxygen saturation with consequent subject awakening and reduction of sleep quality. There are mainly three forms of sleep apnea: 1)The Obstructive Sleep Apnea (OSA), which is characterized by an upper airway airflow reduction caused by the collapse of the soft tissues in the back of the throat and the tongue; 2) The Central Sleep Apnea (CSA), which is characterized by the absence of respiratory effort and, thus, the absence of airflow; 3) The Mixed Sleep Apnea (MSA), which is a combination of the previous two.
Respectively, they represent the 84%, the 0.4% and the 15% of the total cases in U.S
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